18.4.3.10 Range Data Super Resolution, Depth Super Resolution

Liu, W., Chen, X., Yang, J., Wu, Q.,
Variable Bandwidth Weighting for Texture Copy Artifact Suppression in Guided Depth Upsampling,
CirSysVideo(27), No. 10, October 2017, pp. 2072-2085.
IEEE DOI 1710
Bandwidth, Color, Computational efficiency, DH-HEMTs, Image color analysis, Image resolution, Kernel, Blur of depth discontinuities, color image-guided depth upsampling, texture copy artifacts, variable, bandwidth, weighting BibRef

Shabaninia, E.[Elham], Naghsh-Nilchi, A.R.[Ahmad Reza], Kasaei, S.[Shohreh],
High-order Markov random field for single depth image super-resolution,
IET-CV(11), No. 8, December 2017, pp. 683-690.
DOI Link 1712
BibRef

Jiang, Z.Y.[Zhong-Yu], Hou, Y.H.[Yong-Hong], Yue, H.J.[Huan-Jing], Yang, J.Y.[Jing-Yu], Hou, C.P.[Chun-Ping],
Depth Super-Resolution From RGB-D Pairs With Transform and Spatial Domain Regularization,
IP(27), No. 5, May 2018, pp. 2587-2602.
IEEE DOI 1804
autoregressive processes, finite difference methods, gradient methods, image colour analysis, image resolution, sparse representation BibRef

Yue, H.J.[Huan-Jing], Zhou, T.[Tong], Jiang, Z.Y.[Zhong-Yu], Yang, J.Y.[Jing-Yu], Hou, C.P.[Chun-Ping],
Reference guided image super-resolution via efficient dense warping and adaptive fusion,
SP:IC(92), 2021, pp. 116062.
Elsevier DOI 2101
Super-resolution, Reference guidence, Adaptive fusion, Dense warping BibRef

Cruz-Martinez, C.[Claudia], Martínez-Carranza, J.[José], Mayol-Cuevas, W.W.[Walterio W.],
Real-time enhancement of sparse 3D maps using a parallel segmentation scheme based on superpixels,
RealTimeIP(14), No. 3, March 2018, pp. 667-683.
Springer DOI 1804
BibRef

Wang, Y., Zhang, J., Liu, Z., Wu, Q., Zhang, Z., Jia, Y.,
Depth Super-Resolution on RGB-D Video Sequences With Large Displacement 3D Motion,
IP(27), No. 7, July 2018, pp. 3571-3585.
IEEE DOI 1805
Boolean functions, Data structures, Image resolution, Motion compensation, Optical imaging, large displacement 3D motion BibRef

Chang, T.A.[Ting-An], Yang, J.F.[Jar-Ferr],
Precise depth map upsampling and enhancement based on edge-preserving fusion filters,
IET-CV(12), No. 5, August 2018, pp. 651-658.
DOI Link 1807
BibRef

Huang, X.[Xu], Qin, R.J.[Rong-Jun], Xiao, C.L.[Chang-Lin], Lu, X.H.[Xiao-Hu],
Super resolution of laser range data based on image-guided fusion and dense matching,
PandRS(144), 2018, pp. 105-118.
Elsevier DOI 1809
Super resolution, Laser range data, Image, Fusion, Matching BibRef

Zhang, H., Zhang, Y., Wang, H., Ho, Y., Feng, S.,
WLDISR: Weighted Local Sparse Representation-Based Depth Image Super-Resolution for 3D Video System,
IP(28), No. 2, February 2019, pp. 561-576.
IEEE DOI 1811
edge detection, image colour analysis, image reconstruction, image representation, image resolution, image texture, virtual view image quality BibRef

Wen, Y.[Yang], Sheng, B.[Bin], Li, P.[Ping], Lin, W.Y.[Wei-Yao], Feng, D.D.[David Dagan],
Deep Color Guided Coarse-to-Fine Convolutional Network Cascade for Depth Image Super-Resolution,
IP(28), No. 2, February 2019, pp. 994-1006.
IEEE DOI 1811
Image color analysis, Image edge detection, Color, Spatial resolution, Kernel, Sensors, Depth super-resolution, filter kernel learning BibRef

Qiao, Y., Jiao, L., Yang, S., Hou, B.,
A Novel Segmentation Based Depth Map Up-Sampling,
MultMed(21), No. 1, January 2019, pp. 1-14.
IEEE DOI 1901
Image segmentation, Color, Image color analysis, Visualization, Merging, Interpolation, joint trilateral filtering BibRef

Zhao, L.J.[Li-Jun], Bai, H.H.[Hui-Hui], Liang, J.[Jie], Zeng, B.[Bing], Wang, A.H.[An-Hong], Zhao, Y.[Yao],
Simultaneous color-depth super-resolution with conditional generative adversarial networks,
PR(88), 2019, pp. 356-369.
Elsevier DOI 1901
Generative adversarial networks, Super-resolution, Image smoothing, Edge detection BibRef

He, L.Z.[Ling-Zhi], Zhu, H.G.[Hong-Guang], Li, F.[Feng], Bai, H.H.[Hui-Hui], Cong, R.[Runmin], Zhang, C.J.[Chun-Jie], Lin, C.Y.[Chun-Yu], Liu, M.[Meiqin], Zhao, Y.[Yao],
Towards Fast and Accurate Real-World Depth Super-Resolution: Benchmark Dataset and Baseline,
CVPR21(9225-9234)
IEEE DOI 2111
Training, Superresolution, Focusing, Estimation, Benchmark testing, Mobile handsets BibRef

Wang, B., Zou, J., Li, Y., Ju, K., Xiong, H., Zheng, Y.F.,
Sparse-to-Dense Depth Estimation in Videos via High-Dimensional Tensor Voting,
CirSysVideo(29), No. 1, January 2019, pp. 68-79.
IEEE DOI 1901
Tensile stress, Estimation, Videos, Motion estimation, Bidirectional control, Reliability, bilateral filtering BibRef

Xu, Z.[Zekai], Chen, Z.X.[Zi-Xuan], Yi, W.W.[Wei-Wei], Gui, Q.L.[Qiu-Ling], Hou, W.G.[Wen-Guang], Ding, M.Y.[Ming-Yue],
Deep gradient prior network for DEM super-resolution: Transfer learning from image to DEM,
PandRS(150), 2019, pp. 80-90.
Elsevier DOI 1903
Super-resolution, Digital elevation model, Gradient reconstruction, Convolutional neural network, Transfer learning BibRef

Guo, C., Li, C., Guo, J., Cong, R., Fu, H., Han, P.,
Hierarchical Features Driven Residual Learning for Depth Map Super-Resolution,
IP(28), No. 5, May 2019, pp. 2545-2557.
IEEE DOI 1903
Color, Feature extraction, Image reconstruction, Spatial resolution, Task analysis, Cameras, image reconstruction BibRef

Vosters, L.[Luc], Varekamp, C.[Chris], de Haan, G.[Gerard],
Overview of efficient high-quality state-of-the-art depth enhancement methods by thorough design space exploration,
RealTimeIP(16), No. 2, April 2019, pp. 355-375.
WWW Link. 1904
BibRef

Belhi, A.[Abdelhak], Bouras, A.[Abdelaziz], Alfaqheri, T.[Taha], Aondoakaa, A.S.[Akuha Solomon], Sadka, A.H.[Abdul Hamid],
Investigating 3D holoscopic visual content upsampling using super-resolution for cultural heritage digitization,
SP:IC(75), 2019, pp. 188-198.
Elsevier DOI 1906
Cultural heritage, Deep learning, Super-resolution, 3D holoscopic imaging BibRef

Yang, Y.[Yoonmo], Lee, H.S.[Hean Sung], Oh, B.T.[Byung Tae],
Depth map upsampling with a confidence-based joint guided filter,
SP:IC(77), 2019, pp. 40-48.
Elsevier DOI 1906
Upsampling, Super-resolution, Depth map, Confidence map, Guided filter BibRef

Song, X., Dai, Y., Qin, X.,
Deeply Supervised Depth Map Super-Resolution as Novel View Synthesis,
CirSysVideo(29), No. 8, August 2019, pp. 2323-2336.
IEEE DOI 1908
Color, Task analysis, Spatial resolution, Deconvolution, Cameras, DH-HEMTs, Convolutional neural network, depth map, novel view synthesis BibRef

Liu, J.[Jing], Sun, W.N.[Wan-Ning], Su, Y.T.[Yu-Ting], Jing, P.G.[Pei-Guang], Yang, X.K.[Xiao-Kang],
BE-CALF: Bit-Depth Enhancement by Concatenating All Level Features of DNN,
IP(28), No. 10, October 2019, pp. 4926-4940.
IEEE DOI 1909
convolutional neural nets, data visualisation, image enhancement, image resolution, rendering (computer graphics), skip connections BibRef

Liu, J.[Jing], Liu, P.P.[Ping-Ping], Su, Y.T.[Yu-Ting], Jing, P.G.[Pei-Guang], Yang, X.K.[Xiao-Kang],
Spatiotemporal Symmetric Convolutional Neural Network for Video Bit-Depth Enhancement,
MultMed(21), No. 9, September 2019, pp. 2397-2406.
IEEE DOI 1909
Image resolution, Convolutional codes, Spatiotemporal phenomena, Dynamic range, Transforms, Distortion, Correlation, feature fusion BibRef

Liu, J.[Jing], Yang, Z.[Ziwen], Su, Y.T.[Yu-Ting], Yang, X.K.[Xiao-Kang],
TANet: Target Attention Network for Video Bit-Depth Enhancement,
MultMed(24), 2022, pp. 4212-4223.
IEEE DOI 2209
Spatiotemporal phenomena, Task analysis, Feature extraction, Image reconstruction, Distortion, Video bit-depth enhancement, spatiotemporal feature fusion BibRef

Huang, L.Q.[Li-Qin], Zhang, J.J.[Jian-Jia], Zuo, Y.F.[Yi-Fan], Wu, Q.[Qiang],
Pyramid-Structured Depth MAP Super-Resolution Based on Deep Dense-Residual Network,
SPLetters(26), No. 12, December 2019, pp. 1723-1727.
IEEE DOI 2001
convolutional neural nets, image reconstruction, image representation, image resolution, deep convolutional neural networks BibRef

Zuo, Y.F.[Yi-Fan], Wu, Q.[Qiang], Fang, Y.M.[Yu-Ming], An, P.[Ping], Huang, L.Q.[Li-Qin], Chen, Z.F.[Zhi-Feng],
Multi-Scale Frequency Reconstruction for Guided Depth Map Super-Resolution via Deep Residual Network,
CirSysVideo(30), No. 2, February 2020, pp. 297-306.
IEEE DOI 2002
Color, Image reconstruction, Image edge detection, Image resolution, Noise measurement, Dictionaries, Training, batch-normalization BibRef

Zuo, Y.F.[Yi-Fan], Wang, H.[Hao], Fang, Y.M.[Yu-Ming], Huang, X.S.[Xiao-Shui], Shang, X.[Xiwu], Wu, Q.[Qiang],
MIG-Net: Multi-Scale Network Alternatively Guided by Intensity and Gradient Features for Depth Map Super-Resolution,
MultMed(24), 2022, pp. 3506-3519.
IEEE DOI 2207
Superresolution, Image edge detection, Image color analysis, Image coding, Color, Noise reduction, Dictionaries, intensity-guided depth map super-resolution BibRef

Li, B.C.[Bei-Chen], Zhou, Y.[Yuan], Zhang, Y.[Yeda], Wang, A.[Aihua],
Depth image super-resolution based on joint sparse coding,
PRL(130), 2020, pp. 21-29.
Elsevier DOI 2002
Image super-resolution, Joint sparse coding BibRef

Zhang, Y.[Yeda], Zhou, Y.[Yuan], Wang, A.[Aihua], Wu, Q.[Qiong], Hou, C.P.[Chun-Ping],
Joint nonlocal sparse representation for depth map super-resolution,
ICIP17(972-976)
IEEE DOI 1803
Color, Dictionaries, Estimation, Image reconstruction, Principal component analysis, Spatial resolution, sparse representation BibRef

Wang, Z.H.[Zhi-Hui], Ye, X.C.[Xin-Chen], Sun, B.L.[Bao-Li], Yang, J.Y.[Jing-Yu], Xu, R.[Rui], Li, H.J.[Hao-Jie],
Depth upsampling based on deep edge-aware learning,
PR(103), 2020, pp. 107274.
Elsevier DOI 2005
Upsampling, CNN, Edge-aware, Depth map BibRef

Yang, H.[Hang], Zhang, Z.B.[Zhong-Bo],
Depth image upsampling based on guided filter with low gradient minimization,
VC(36), No. 7, July 2020, pp. 1411-1422.
WWW Link. 2005
BibRef

Wang, J.[Jin], Xu, W.[Wei], Cai, J.F.[Jian-Feng], Zhu, Q.[Qing], Shi, Y.H.[Yun-Hui], Yin, B.C.[Bao-Cai],
Multi-Direction Dictionary Learning Based Depth Map Super-Resolution With Autoregressive Modeling,
MultMed(22), No. 6, June 2020, pp. 1470-1484.
IEEE DOI 2005
Dictionaries, Adaptation models, Image edge detection, Color, Machine learning, Cameras, Geometry, Depth map, sparse representation BibRef

Xu, W.[Wei], Wang, J.[Jin], Sun, L.H.[Long-Hua], Zhu, Q.[Qing],
Depth Map Super-Resolution By Multi-Direction Dictionary and Joint Regularization,
ICIP21(1839-1843)
IEEE DOI 2201
Visualization, Dictionaries, TV, Superresolution, Cameras, Depth map, super-resolution (SR), dictionary training, regularization, sparse representation BibRef

Xu, W.[Wei], Wang, J.[Jin], Zhu, Q.[Qing], Wu, X.[Xi], Qi, Y.F.[Yi-Fei],
Depth map super-resolution via multiclass dictionary learning with geometrical directions,
VCIP17(1-4)
IEEE DOI 1804
autoregressive processes, image colour analysis, image reconstruction, image representation, image resolution, super-resolution (SR) BibRef

Gu, X., Guo, Y., Deligianni, F., Yang, G.,
Coupled Real-Synthetic Domain Adaptation for Real-World Deep Depth Enhancement,
IP(29), 2020, pp. 6343-6356.
IEEE DOI 2006
Pipelines, Training, Deep learning, Sensors, Degradation, Adaptation models, Depth enhancement, real-world, denoising, deep learning BibRef

Ye, X., Sun, B., Wang, Z., Yang, J., Xu, R., Li, H., Li, B.,
PMBANet: Progressive Multi-Branch Aggregation Network for Scene Depth Super-Resolution,
IP(29), 2020, pp. 7427-7442.
IEEE DOI 2007
Depth map, super-resolution, aggregation, progressive, multi-branch BibRef

Li, T.[Tao], Lin, H.W.[Hong-Wei], Dong, X.C.[Xiu-Cheng], Zhang, X.H.[Xiao-Hua],
Depth image super-resolution using correlation-controlled color guidance and multi-scale symmetric network,
PR(107), 2020, pp. 107513.
Elsevier DOI 2008
Depth image super-resolution, Deep convolutional neural network, Encoder-decoder structure, Channel correlation BibRef

Deng, X., Song, P., Rodrigues, M.R.D., Dragotti, P.L.,
RADAR: Robust Algorithm for Depth Image Super Resolution Based on FRI Theory and Multimodal Dictionary Learning,
CirSysVideo(30), No. 8, August 2020, pp. 2447-2462.
IEEE DOI 2008
Image resolution, Training, Machine learning, Color, Image reconstruction, Noise measurement, Image edge detection, multimodal image processing BibRef

Haefner, B.[Bjoern], Peng, S.Y.[Song-You], Verma, A.[Alok], Quéau, Y.[Yvain], Cremers, D.[Daniel],
Photometric Depth Super-Resolution,
PAMI(42), No. 10, October 2020, pp. 2453-2464.
IEEE DOI 2009
Image resolution, Lighting, Shape, Training, Cameras, Color, Frequency measurement, RGB-D cameras, depth super-resolution, deep learning BibRef

Sang, L., Haefner, B., Cremers, D.,
Inferring Super-Resolution Depth from a Moving Light-Source Enhanced RGB-D Sensor: A Variational Approach,
WACV20(1-10)
IEEE DOI 2006
Cameras, Lighting, Image resolution, Light sources, Geometry, Light emitting diodes, Calibration BibRef

Chen, J.[Jian], Zhang, Z.[Zichao], Zhang, K.[Kai], Wang, S.[Shubo], Han, Y.[Yu],
UAV-Borne LiDAR Crop Point Cloud Enhancement Using Grasshopper Optimization and Point Cloud Up-Sampling Network,
RS(12), No. 19, 2020, pp. xx-yy.
DOI Link 2010
BibRef

Jiang, Z.Y.[Zhong-Yu], Yue, H.J.[Huan-Jing], Lai, Y.K.[Yu-Kun], Yang, J.Y.[Jing-Yu], Hou, Y.H.[Yong-Hong], Hou, C.P.[Chun-Ping],
Deep edge map guided depth super resolution,
SP:IC(90), 2021, pp. 116040.
Elsevier DOI 2012
Super resolution, Depth map, Edge prediction, Disentangling BibRef

Zuo, Y., Fang, Y., Yang, Y., Shang, X., Wu, Q.,
Depth Map Enhancement by Revisiting Multi-Scale Intensity Guidance Within Coarse-to-Fine Stages,
CirSysVideo(30), No. 12, December 2020, pp. 4676-4687.
IEEE DOI 2012
Color, Image edge detection, Feature extraction, Image reconstruction, Encoding, Dictionaries, dense connection BibRef

Yeo, Y.J., Sagong, M.C., Shin, Y.G., Jung, S.W., Ko, S.J.,
Simple Yet Effective Way for Improving the Performance of Depth Map Super-Resolution,
SPLetters(27), 2020, pp. 2099-2103.
IEEE DOI 2012
Image color analysis, Color, Convolution, Feature extraction, PSNR, Image edge detection, Standards, Depth map super-resolution (SR), deep learning BibRef

Balure, C.S.[Chandra Shaker], Kini, M.R.[M. Ramesh],
Guidance-based improved depth upsampling with better initial estimate,
IJCVR(11), No. 1, 2021, pp. 109-125.
DOI Link 2012
BibRef

Li, S.M.[Shi-Ming], Ge, X.M.[Xu-Ming], Hu, H.[Han], Zhu, Q.[Qing],
Laplacian fusion approach of multi-source point clouds for detail enhancement,
PandRS(171), 2021, pp. 385-396.
Elsevier DOI 2012
Multi-sources, Point Clouds, Reconstruction, Laplacian fusion BibRef

Zuo, Y., Fang, Y., An, P., Shang, X., Yang, J.,
Frequency-Dependent Depth Map Enhancement via Iterative Depth-Guided Affine Transformation and Intensity-Guided Refinement,
MultMed(23), 2021, pp. 772-783.
IEEE DOI 2102
Color, Image edge detection, Image resolution, Optimization, Robustness, Encoding, Dictionaries, dense connection BibRef

Zhang, F.[Fan], Liu, N.[Na], Chang, L.[Liang], Duan, F.Q.[Fu-Qing], Deng, X.M.[Xiao-Ming],
Edge-guided single facial depth map super-resolution using CNN,
IET-IPR(14), No. 17, 24 December 2020, pp. 4708-4716.
DOI Link 2104
BibRef

Li, Y.H.[Yin-Hao], Iwamoto, Y.[Yutaro], Lin, L.F.[Lan-Fen], Xu, R.[Rui], Tong, R.F.[Ruo-Feng], Chen, Y.W.[Yen-Wei],
VolumeNet: A Lightweight Parallel Network for Super-Resolution of MR and CT Volumetric Data,
IP(30), 2021, pp. 4840-4854.
IEEE DOI 2105
BibRef

Li, X.Q.[Xiao-Qiang], Liu, J.[Jitao], Dai, S.M.[Song-Min],
Point cloud super-resolution based on geometric constraints,
IET-CV(15), No. 4, 2021, pp. 312-321.
DOI Link 2106
BibRef

Li, W.[Weite], Hasegawa, K.[Kyoko], Li, L.[Liang], Tsukamoto, A.[Akihiro], Tanaka, S.[Satoshi],
Deep Learning-Based Point Upsampling for Edge Enhancement of 3D-Scanned Data and Its Application to Transparent Visualization,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Zhang, Y.Y.[Ying-Ying], Ren, C.[Chao], Chen, H.G.[Hong-Gang], Zhu, C.[Ce], Liu, K.[Kai],
Single depth map super-resolution via joint non-local self-similarity modeling and local multi-directional gradient-guided regularization,
SP:IC(97), 2021, pp. 116313.
Elsevier DOI 2107
Single depth map, Super-resolution, Non-local self-similarity, Local constraint, Multi-directional gradient-guided regularization BibRef

Zhang, R.C.[Rui-Chen], Bian, S.F.[Shao-Feng], Li, H.[Houpu],
RSPCN: Super-Resolution of Digital Elevation Model Based on Recursive Sub-Pixel Convolutional Neural Networks,
IJGI(10), No. 8, 2021, pp. xx-yy.
DOI Link 2108
BibRef

Zhou, A.[Annan], Chen, Y.[Yumin], Wilson, J.P.[John P.], Su, H.[Heng], Xiong, Z.[Zhexin], Cheng, Q.[Qishan],
An Enhanced Double-Filter Deep Residual Neural Network for Generating Super Resolution DEMs,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Qian, Y.[Yue], Hou, J.H.[Jun-Hui], Kwong, S.[Sam], He, Y.[Ying],
Deep Magnification-Flexible Upsampling Over 3D Point Clouds,
IP(30), 2021, pp. 8354-8367.
IEEE DOI 2110
Geometry, Feature extraction, Training, Surface reconstruction, Image reconstruction, Deep learning, surface reconstruction BibRef

Ding, D.D.[Dan-Dan], Qiu, C.[Chi], Liu, F.[Fuchang], Pan, Z.[Zhigeng],
Point Cloud Upsampling via Perturbation Learning,
CirSysVideo(31), No. 12, December 2021, pp. 4661-4672.
IEEE DOI 2112
Feature extraction, Perturbation methods, Image reconstruction, Geometry, neural network BibRef

Zhang, P.P.[Ping-Ping], Wang, X.[Xu], Ma, L.[Lin], Wang, S.Q.[Shi-Qi], Kwong, S.[Sam], Jiang, J.[Jianmin],
Progressive Point Cloud Upsampling via Differentiable Rendering,
CirSysVideo(31), No. 12, December 2021, pp. 4673-4685.
IEEE DOI 2112
Rendering (computer graphics), Task analysis, Image reconstruction, Surface reconstruction, feature expansion unit BibRef

Wang, K.[Kaisiyuan], Sheng, L.[Lu], Gu, S.H.[Shu-Hang], Xu, D.[Dong],
Sequential Point Cloud Upsampling by Exploiting Multi-Scale Temporal Dependency,
CirSysVideo(31), No. 12, December 2021, pp. 4686-4696.
IEEE DOI 2112
Feature extraction, Shape, Task analysis, Superresolution, Estimation, Solid modeling, spatio-temporal exploration BibRef

Deng, Q.W.[Qin-Wen], Zhang, S.Y.[Song-Yang], Ding, Z.[Zhi],
Point Cloud Resampling via Hypergraph Signal Processing,
SPLetters(28), 2021, pp. 2117-2121.
IEEE DOI 2112
Kernel, Surface reconstruction, Tensors, Feature extraction, Signal processing algorithms, virtual reality BibRef

Zhong, Z.W.[Zhi-Wei], Liu, X.[Xianming], Jiang, J.J.[Jun-Jun], Zhao, D.B.[De-Bin], Chen, Z.W.[Zhi-Wen], Ji, X.Y.[Xiang-Yang],
High-Resolution Depth Maps Imaging via Attention-Based Hierarchical Multi-Modal Fusion,
IP(31), 2022, pp. 648-663.
IEEE DOI 2201
Image reconstruction, Feature extraction, Convolution, Superresolution, Kernel, Collaboration, Depth map super-resolution, bi-directional feature propagation BibRef

Yang, B.S.[Bi-Sheng], Li, J.P.[Jian-Ping],
A hierarchical approach for refining point cloud quality of a low cost UAV LiDAR system in the urban environment,
PandRS(183), 2022, pp. 403-421.
Elsevier DOI 2201
Low cost, Unmanned aerial vehicle (UAV), Light detection and ranging (LiDAR), Point clouds, Matching BibRef

Tao, Y.[Yu], Xiong, S.T.[Si-Ting], Muller, J.P.[Jan-Peter], Michael, G.[Greg], Conway, S.J.[Susan J.], Paar, G.[Gerhard], Cremonese, G.[Gabriele], Thomas, N.[Nicolas],
Subpixel-Scale Topography Retrieval of Mars Using Single-Image DTM Estimation and Super-Resolution Restoration,
RS(14), No. 2, 2022, pp. xx-yy.
DOI Link 2201
BibRef

Han, B.[Bing], Zhang, X.Y.[Xin-Yun], Ren, S.[Shuang],
PU-GACNet: Graph Attention Convolution Network for Point Cloud Upsampling,
IVC(118), 2022, pp. 104371.
Elsevier DOI 2202
Point cloud upsampling, Graph attention convolution, Feature extraction, Edge-aware nodeshuffle, Feature expansion BibRef

Borges, T.M.[Tomás M.], Garcia, D.C.[Diogo C.], de Queiroz, R.L.[Ricardo L.],
Fractional Super-Resolution of Voxelized Point Clouds,
IP(31), 2022, pp. 1380-1390.
IEEE DOI 2202
Geometry, Point cloud compression, Rendering (computer graphics), Octrees, Superresolution, resampling BibRef

Liu, P.[Peng], Zhang, Z.[Zonghua], Meng, Z.[Zhaozong], Gao, N.[Nan],
Deformable Enhancement and Adaptive Fusion for Depth Map Super-Resolution,
SPLetters(29), 2022, pp. 204-208.
IEEE DOI 2202
Superresolution, Convolution, Signal resolution, Image restoration, Pattern recognition, Convolutional neural networks, fusion BibRef

Yang, Y.X.[Yu-Xiang], Cao, Q.[Qi], Zhang, J.[Jing], Tao, D.C.[Da-Cheng],
CODON: On Orchestrating Cross-Domain Attentions for Depth Super-Resolution,
IJCV(130), No. 2, February 2022, pp. 267-284.
Springer DOI 2202
BibRef

Kalenjuk, S.[Slaven], Lienhart, W.[Werner],
A Method for Efficient Quality Control and Enhancement of Mobile Laser Scanning Data,
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Wang, Y.R.[Ying-Rui], Wang, S.[Suyu], Sun, L.H.[Long-Hua],
Point Cloud Upsampling via a Coarse-to-Fine Network,
MMMod22(I:467-478).
Springer DOI 2203
BibRef

Lin, X.[Xu], Zhang, Q.Q.[Qing-Qing], Wang, H.Y.[Hong-Yue], Yao, C.L.[Chao-Long], Chen, C.X.[Chang-Xin], Cheng, L.[Lin], Li, Z.X.[Zhao-Xiong],
A DEM Super-Resolution Reconstruction Network Combining Internal and External Learning,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Wang, J.[Jin], Sun, L.H.[Long-Hua], Xiong, R.Q.[Rui-Qin], Shi, Y.H.[Yun-Hui], Zhu, Q.[Qing], Yin, B.C.[Bao-Cai],
Depth Map Super-Resolution Based on Dual Normal-Depth Regularization and Graph Laplacian Prior,
CirSysVideo(32), No. 6, June 2022, pp. 3304-3318.
IEEE DOI 2206
Color, Laplace equations, Image edge detection, Optimization, Image restoration, Image reconstruction, Cameras, Depth map, reweighted graph Laplacian regularizer (RWGLR) BibRef

Zhang, Y.F.[Yi-Fan], Yu, W.H.[Wen-Hao], Zhu, D.[Di],
Terrain feature-aware deep learning network for digital elevation model superresolution,
PandRS(189), 2022, pp. 143-162.
Elsevier DOI 2206
DEM superresolution, Terrain features, Explicit terrain optimization, Deformable convolution BibRef

Murayama, M.[Masahiro], Higashiyama, T.[Toyohiro], Harazono, Y.[Yuki], Ishii, H.[Hirotake], Shimoda, H.[Hiroshi], Okido, S.[Shinobu], Taruta, Y.[Yasuyoshi],
Depth Image Noise Reduction and Super-Resolution by Pixel-Wise Multi-Frame Fusion,
IEICE(E105-D), No. 6, June 2022, pp. 1211-1224.
WWW Link. 2206
BibRef

Wang, K.Y.[Kaisi-Yuan], Sheng, L.[Lu], Gu, S.H.[Shu-Hang], Xu, D.[Dong],
VPU: A Video-Based Point Cloud Upsampling Framework,
IP(31), 2022, pp. 4062-4075.
IEEE DOI 2206
Point cloud compression, Feature extraction, Task analysis, Graphics processing units, Image reconstruction, Cloud computing, spatial-temporal aggregation BibRef

Dinesh, C.[Chinthaka], Cheung, G.[Gene], Bajic, I.V.[Ivan V.],
Point Cloud Video Super-Resolution via Partial Point Coupling and Graph Smoothness,
IP(31), 2022, pp. 4117-4132.
IEEE DOI 2206
BibRef
Earlier:
3D Point Cloud Super-Resolution via Graph Total Variation on Surface Normals,
ICIP19(4390-4394)
IEEE DOI 1910
graph signal processing, point cloud super-resolution, graph total variation, convex optimization Geometry, Noise reduction, Laplace equations, Image restoration, Surface treatment, Computational modeling, 3D point cloud, numerical linear algebra BibRef

Akhtar, A.[Anique], Li, Z.[Zhu], van der Auwera, G.[Geert], Li, L.[Li], Chen, J.L.[Jian-Le],
PU-Dense: Sparse Tensor-Based Point Cloud Geometry Upsampling,
IP(31), 2022, pp. 4133-4148.
IEEE DOI 2206
Point cloud compression, Laser radar, Geometry, Feature extraction, Sensors, Deep learning, Point cloud, upsampling BibRef

Yang, L.[Ling], Zhang, F.[Fubo], Zhang, Z.[Zhuo], Chen, L.Y.[Long-Yong], Wang, D.W.[Da-Wei], Yang, Y.Q.[Ya-Qian], Li, Z.H.[Zhen-Hua],
Elevation Resolution Enhancement Method Using Non-Ideal Linear Motion Error of Airborne Array TomoSAR,
RS(14), No. 12, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Song, X.B.[Xi-Bin], Zhou, D.F.[Ding-Fu], Li, W.[Wei], Dai, Y.C.[Yu-Chao], Liu, L.[Liu], Li, H.D.[Hong-Dong], Yang, R.G.[Rui-Gang], Zhang, L.J.[Liang-Jun],
WAFP-Net: Weighted Attention Fusion Based Progressive Residual Learning for Depth Map Super-Resolution,
MultMed(24), 2022, pp. 4113-4127.
IEEE DOI 2208
BibRef
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Channel Attention Based Iterative Residual Learning for Depth Map Super-Resolution,
CVPR20(5630-5639)
IEEE DOI 2008
Degradation, Superresolution, Color, Feature extraction, Laser radar, Image edge detection, Attention fusion, depth, super-resolution, residual learning. Color, Kernel, Image resolution, Feature extraction BibRef

Zhang, X.[Xue], Cheung, G.[Gene], Pang, J.H.[Jia-Hao], Sanghvi, Y.[Yash], Gnanasambandam, A.[Abhiram], Chan, S.H.[Stanley H.],
Graph-Based Depth Denoising and Dequantization for Point Cloud Enhancement,
IP(31), 2022, pp. 6863-6878.
IEEE DOI 2212
Sensors, Noise reduction, Point cloud compression, Quantization (signal), Noise measurement, Image sensors, graph signal processing BibRef

Zhang, X.[Xue], Cheung, G.[Gene], Pang, J.H.[Jia-Hao], Tian, D.,
3D Point Cloud Enhancement Using Graph-Modelled Multiview Depth Measurements,
ICIP20(3314-3318)
IEEE DOI 2011
Cameras, Sensors, Optimization, Measurement, Noise reduction, Laplace equations, 3D point cloud, convex optimization BibRef

Agresti, G.[Gianluca], Schäfer, H.[Henrik], Sartor, P.[Piergiorgio], Incesu, Y.[Yalcin], Zanuttigh, P.[Pietro],
Unsupervised Domain Adaptation of Deep Networks for ToF Depth Refinement,
PAMI(44), No. 12, December 2022, pp. 9195-9208.
IEEE DOI 2212
Noise reduction, Distortion, Frequency modulation, Frequency-domain analysis, Deep learning, Thermal sensors, adversarial learning BibRef

Wang, H.T.[Hao-Tian], Yang, M.[Meng], Lan, X.G.[Xu-Guang], Zhu, C.[Ce], Zheng, N.N.[Nan-Ning],
Depth Map Recovery Based on a Unified Depth Boundary Distortion Model,
IP(31), 2022, pp. 7020-7035.
IEEE DOI 2212
Image analysis, Nonlinear distortion, Sensors, Task analysis, Superresolution, Learning systems, Indexes, Depth map recovery, depth super-resolution BibRef

Gu, F.[Fan], Zhang, C.[Changlun], Wang, H.[Hengyou], He, Q.[Qiang], Huo, L.[Lianzhi],
PU-WGCN: Point Cloud Upsampling Using Weighted Graph Convolutional Networks,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link 2212
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Xu, W.[Wei], Zhu, Q.[Qing], Qi, N.[Na],
Depth Map Super-Resolution via Joint Local Gradient and Nonlocal Structural Regularizations,
CirSysVideo(32), No. 12, December 2022, pp. 8297-8311.
IEEE DOI 2212
Dictionaries, Cameras, Color, TV, Image edge detection, Task analysis, Image color analysis, Depth map, dictionary learning, super-resolution BibRef

Liu, H.[Hao], Yuan, H.[Hui], Hou, J.H.[Jun-Hui], Hamzaoui, R.[Raouf], Gao, W.[Wei],
PUFA-GAN: A Frequency-Aware Generative Adversarial Network for 3D Point Cloud Upsampling,
IP(31), 2022, pp. 7389-7402.
IEEE DOI 2212
Point cloud compression, Geometry, Measurement, Training, Visualization, Feature extraction, Point cloud upsampling, deep learning BibRef

Kim, J.[Jiwan], Kim, M.C.[Min-Chang], Shin, Y.G.[Yeong-Gil], Chung, M.Y.[Min-Young],
Accurate depth image generation via overfit training of point cloud registration using local frame sets,
CVIU(226), 2023, pp. 103588.
Elsevier DOI 2212
Depth image enhancement, Enhanced depth dataset, RGB-D image, Unsupervised depth registration BibRef

Wang, J.[Jun], Liu, P.[Peilin], Wen, F.[Fei],
Self-Supervised Learning for RGB-Guided Depth Enhancement by Exploiting the Dependency Between RGB and Depth,
IP(32), 2023, pp. 159-174.
IEEE DOI 2301
Degradation, Noise reduction, Image enhancement, Sensors, Filling, Interference, Imaging, Depth image enhancement, RGB-guided, mutual information BibRef

Wang, K.[Ke], Zhao, L.J.[Li-Jun], Zhang, J.J.[Jin-Jing], Zhang, J.L.[Jia-Long], Wang, A.H.[An-Hong], Bai, H.H.[Hui-Hui],
Joint depth map super-resolution method via deep hybrid-cross guidance filter,
PR(136), 2023, pp. 109260.
Elsevier DOI 2301
Joint image filter, Depth image, Super-resolution, Hybrid-cross guidance, Space-aware group-compensation BibRef

Han, X.Y.[Xiao-Yi], Ma, X.C.[Xiao-Chuan], Li, H.[Houpu], Chen, Z.L.[Zhan-Long],
A Global-Information-Constrained Deep Learning Network for Digital Elevation Model Super-Resolution,
RS(15), No. 2, 2023, pp. xx-yy.
DOI Link 2301
BibRef

Sarmad, M.[Muhammad], Ruspini, L.C.[Leonardo Carlos], Lindseth, F.[Frank],
SIT-SR 3D: Self-supervised slice interpolation via transfer learning for 3D volume super-resolution,
PRL(166), 2023, pp. 97-104.
Elsevier DOI 2302
Super-resolution, Digital rock analysis, Self-supervised learning BibRef

Chen, G.D.[Guo-Dong], Chen, Y.[Yumin], Wilson, J.P.[John P.], Zhou, A.[Annan], Chen, Y.[Yuejun], Su, H.[Heng],
An Enhanced Residual Feature Fusion Network Integrated with a Terrain Weight Module for Digital Elevation Model Super-Resolution,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link 2303
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Lim, S.G.[Sung-Gyun], Kim, D.H.[Dong-Ha], Oh, K.J.[Kwan-Jung], Lee, G.S.[Gwang-Soon], Jeong, J.Y.[Jun Young], Kim, J.G.[Jae-Gon],
Wider Depth Dynamic Range Using Occupancy Map Correction for Immersive Video Coding,
IEICE(E106-D), No. 5, May 2023, pp. 1102-1105.
WWW Link. 2305
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Zhang, F.[Fan], Liu, N.[Na], Duan, F.Q.[Fu-Qing],
Coarse-to-fine Face Depth Super-Resolution with Attentive Feature Selection,
ICPR22(3966-3972)
IEEE DOI 2212
Visualization, Face recognition, Superresolution, Stacking, Noise reduction, Feature extraction, Robustness BibRef

Heimann, V.[Viktoria], Spruck, A.[Andreas], Kaup, A.[André],
Frequency-Selective Geometry Upsampling of Point Clouds,
ICIP22(1511-1515)
IEEE DOI 2211
Point cloud compression, Geometry, Visualization, Image color analysis, Frequency estimation, frequency selectivity BibRef

Shtendel, G.[Gal], Bhandari, A.[Ayush],
HDR-TOF: HDR Time-of-Flight Imaging via Modulo Acquisition,
ICIP22(3808-3812)
IEEE DOI 2211
Image sensors, Heuristic algorithms, Current measurement, Imaging, Lighting, 3D/depth imaging, computational imaging, time-of-flight BibRef

Feng, W.Q.[Wan-Quan], Li, J.[Jin], Cai, H.R.[Hong-Rui], Luo, X.N.[Xiao-Nan], Zhang, J.[Juyong],
Neural Points: Point Cloud Representation with Neural Fields for Arbitrary Upsampling,
CVPR22(18612-18621)
IEEE DOI 2210
Point cloud compression, Codes, Shape, Feature extraction, Robustness, Vision + graphics, Low-level vision, RGBD sensors and analytics BibRef

Zhao, W.[Wenbo], Liu, X.[Xianming], Zhong, Z.W.[Zhi-Wei], Jiang, J.J.[Jun-Jun], Gao, W.[Wei], Li, G.[Ge], Ji, X.Y.[Xiang-Yang],
Self-Supervised Arbitrary-Scale Point Clouds Upsampling via Implicit Neural Representation,
CVPR22(1989-1997)
IEEE DOI 2210
Point cloud compression, Visualization, Codes, Supervised learning, Self-supervised learning, Pattern recognition, Low-level vision, Self- semi- meta- unsupervised learning BibRef

Xie, W.[Wuyuan], Huang, T.[Tengcong], Wang, M.[Miaohui],
MNSRNet: Multimodal Transformer Network for 3D Surface Super-Resolution,
CVPR22(12693-12702)
IEEE DOI 2210
Geometry, Surface reconstruction, Fuses, Superresolution, Neural networks, Transforms, Vision applications and systems BibRef

Zhao, Z.X.[Zi-Xiang], Zhang, J.[Jiangshe], Xu, S.[Shuang], Lin, Z.[Zudi], Pfister, H.[Hanspeter],
Discrete Cosine Transform Network for Guided Depth Map Super-Resolution,
CVPR22(5687-5697)
IEEE DOI 2210
Convolutional codes, Image edge detection, Superresolution, Feature extraction, Discrete cosine transforms, Data mining, Computational photography BibRef

Luo, L.Q.[Lu-Qing], Tang, L.[Lulu], Zhou, W.Y.[Wan-Yi], Wang, S.Z.[Shi-Zheng], Yang, Z.X.[Zhi-Xin],
PU-EVA: An Edge-Vector based Approximation Solution for Flexible-scale Point Cloud Upsampling,
ICCV21(16188-16197)
IEEE DOI 2203
Point cloud compression, Training, Surface reconstruction, Semantics, Linear approximation, Network architecture, 3D from multiview and other sensors BibRef

Cui, M.L.[Mao-Lin], Xie, W.Y.[Wu-Yuan], Wang, M.H.[Miao-Hui], Huang, T.C.[Teng-Cong],
Residual Geometric Feature Transform Network for 3D Surface Super-Resolution,
3DV21(859-868)
IEEE DOI 2201
Point cloud compression, Surface reconstruction, Shape, Soft sensors, Superresolution, Transforms BibRef

Walecki, P.[Peter], Taubin, G.[Gabriel],
GCSR: Gray Code Super-Resolution 3D Scanning,
3DV21(1156-1164)
IEEE DOI 2201
Solid modeling, Surface reconstruction, Superresolution, Calibration, Reflective binary codes, Standards BibRef

Li, R.H.[Rui-Hui], Li, X.Z.[Xian-Zhi], Heng, P.A.[Pheng-Ann], Fu, C.W.[Chi-Wing],
Point Cloud Upsampling via Disentangled Refinement,
CVPR21(344-353)
IEEE DOI 2111
Surface reconstruction, Pipelines, Generators, Surface roughness, Rough surfaces, Pattern recognition BibRef

Chen, Z.Q.[Zhi-Qin], Kim, V.G.[Vladimir G.], Fisher, M.[Matthew], Aigerman, N.[Noam], Zhang, H.[Hao], Chaudhuri, S.[Siddhartha],
DECOR-GAN: 3D Shape Detailization by Conditional Refinement,
CVPR21(15735-15744)
IEEE DOI 2111
Training, Geometry, Solid modeling, Codes, Shape, Generative adversarial networks BibRef

Sun, B.L.[Bao-Li], Ye, X.C.[Xin-Chen], Li, B.P.[Bao-Pu], Li, H.J.[Hao-Jie], Wang, Z.H.[Zhi-Hui], Xu, R.[Rui],
Learning Scene Structure Guidance via Cross-Task Knowledge Transfer for Single Depth Super-Resolution,
CVPR21(7788-7797)
IEEE DOI 2111
Training, Knowledge engineering, Runtime, Superresolution, Network architecture, Pattern recognition, Task analysis BibRef

Chen, Z., Liu, P., Wen, F., Wang, J., Ying, R.,
Restoration of Motion Blur in Time-of-Flight Depth Image Using Data Alignment,
3DV20(820-828)
IEEE DOI 2102
Cameras, Phase measurement, Image restoration, Sensors, Optical imaging, Adaptive optics, deblurring BibRef

Kubade, A.[Ashish], Patel, D.[Diptiben], Sharma, A.[Avinash], Rajan, K.S.,
AFN: Attentional Feedback Network Based 3d Terrain Super-resolution,
ACCV20(I:192-208).
Springer DOI 2103
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Hui, L.[Le], Xu, R.[Rui], Xie, J.[Jin], Qian, J.J.[Jian-Jun], Yang, J.[Jian],
Progressive Point Cloud Deconvolution Generation Network,
ECCV20(XV:397-413).
Springer DOI 2011
Code, Point Cloud.
WWW Link. Interpolation. BibRef

Qian, Y.[Yue], Hou, J.H.[Jun-Hui], Kwong, S.[Sam], He, Y.[Ying],
Pugeo-net: A Geometry-centric Network for 3d Point Cloud Upsampling,
ECCV20(XIX:752-769).
Springer DOI 2011
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Li, R., Li, X., Fu, C., Cohen-Or, D., Heng, P.,
PU-GAN: A Point Cloud Upsampling Adversarial Network,
ICCV19(7202-7211)
IEEE DOI 2004
feature extraction, image reconstruction, neural nets, latent space, upsample points, working GAN network, Network architecture BibRef

Voynov, O., Artemov, A., Egiazarian, V., Notchenko, A., Bobrovskikh, G., Burnaev, E., Zorin, D.,
Perceptual Deep Depth Super-Resolution,
ICCV19(5652-5662)
IEEE DOI 2004
convolutional neural nets, image colour analysis, image reconstruction, image resolution, image sampling, Optimization BibRef

Wang, Y.F.[Yi-Fan], Wu, S.H.[Shi-Hao], Huang, H.[Hui], Cohen-Or, D.[Daniel], Sorkine-Hornung, O.[Olga],
Patch-Based Progressive 3D Point Set Upsampling,
CVPR19(5951-5960).
IEEE DOI 2002
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Li, J., Zhang, X., Tran, T.,
Point Cloud Denoising Based on Tensor Tucker Decomposition,
ICIP19(4375-4379)
IEEE DOI 1910
Point cloud denoising, Tucker decomposition, Hard thresholding, HOOI algorithm BibRef

Yu, L., Li, X., Fu, C., Cohen-Or, D., Heng, P.,
PU-Net: Point Cloud Upsampling Network,
CVPR18(2790-2799)
IEEE DOI 1812
Feature extraction, Convolution, Geometry, Training, Surface reconstruction, Image reconstruction BibRef

Yan, S.[Shi], Wu, C.L.[Cheng-Lei], Wang, L.Z.[Li-Zhen], Xu, F.[Feng], An, L.[Liang], Guo, K.W.[Kai-Wen], Liu, Y.B.[Ye-Bin],
DDRNet: Depth Map Denoising and Refinement for Consumer Depth Cameras Using Cascaded CNNs,
ECCV18(X: 155-171).
Springer DOI 1810
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Jeon, J.[Junho], Lee, S.Y.[Seung-Yong],
Reconstruction-Based Pairwise Depth Dataset for Depth Image Enhancement Using CNN,
ECCV18(XVI: 438-454).
Springer DOI 1810
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Rapp, J., Dawson, R.M.A., Goyal, V.K.,
Improving LIDAR Depth Resolution with Dither,
ICIP18(1553-1557)
IEEE DOI 1809
Laser radar, Quantization (signal), Photonics, Laser modes, Laser noise, Measurement by laser beam, Detectors, generalized Gaussian BibRef

Bolsee, Q., Munteanu, A.,
CNN-based Denoising of Time-Of-Flight Depth Images,
ICIP18(510-514)
IEEE DOI 1809
Noise reduction, Training, Convolution, Sensors, Cameras, Filtering, Gaussian noise, Time-of-Flight, denoising, residual learning, Convolutional Neural Network BibRef

Garcia, D.C., Fonseca, T.A., de Queiroz, R.L.,
Example-Based Super-Resolution for Point-Cloud Video,
ICIP18(2959-2963)
IEEE DOI 1809
Signal resolution, Spatial resolution, Gain, Measurement, Octrees, super-resolution (SR) BibRef

Chen, R., Zhai, D., Liu, X., Zhao, D.,
Noise-Aware Super-Resolution of Depth Maps Via Graph-Based Plug-And-Play Framework,
ICIP18(2536-2540)
IEEE DOI 1809
Image resolution, Laplace equations, Task analysis, Image restoration, Image edge detection, graph signal processing BibRef

Xu, D., Fan, X., Zhao, D., Gao, W.,
Multiresolution Contourlet Transform Fusion Based Depth Map Super Resolution,
ICIP18(2187-2191)
IEEE DOI 1809
Transforms, Spatial resolution, Color, Laplace equations, Fans, contourlet transform, fusion, super resolution BibRef

Jin, Z., Luo, L., Tang, Y., Zou, W., Li, X.,
A CNN cascade for quality enhancement of compressed depth images,
VCIP17(1-4)
IEEE DOI 1804
convolution, data compression, feedforward neural nets, filtering theory, image coding, image denoising, image recognition, Quality enhancement BibRef

Boubou, S.[Somar], Narikiyo, T.[Tatsuo], Kawanishi, M.[Michihiro],
Adaptive filter for denoising 3D data captured by depth sensors,
3DTV-CON17(1-4)
IEEE DOI 1804
adaptive filters, object recognition, signal denoising, spatial variables measurement, support vector machines, 3D depth sensors BibRef

Yang, J., Lan, H., Song, X., Li, K.,
Depth super-resolution via fully edge-augmented guidance,
VCIP17(1-4)
IEEE DOI 1804
edge detection, feedforward neural nets, image colour analysis, image resolution, learning (artificial intelligence), CNNs, fully guidance structure BibRef

Tsuchiya, A., Sugimura, D., Hamamoto, T.,
Depth upsampling by depth prediction,
ICIP17(1662-1666)
IEEE DOI 1803
Cameras, Color, DH-HEMTs, Estimation, Image color analysis, Image sequences, Motion estimation, Depth prediction, Spatio-temporal coherency BibRef

Zhang, H.T., Yu, J., Wang, Z.F.,
Depth map super-resolution using non-local higher-order regularization with classified weights,
ICIP17(4043-4047)
IEEE DOI 1803
Adaptation models, Color, Feature extraction, Image color analysis, Image edge detection, Image resolution, Tuning, non-local generalized total variation BibRef

Zhu, J., Zhang, J., Cao, Y., Wang, Z.,
Image guided depth enhancement via deep fusion and local linear regularizaron,
ICIP17(4068-4072)
IEEE DOI 1803
Color, Correlation, Feature extraction, Image edge detection, Image resolution, Noise reduction, Training, deep feature space, local linear regularization BibRef

Zhu, J.[Jiang], Zhai, W.[Wei], Cao, Y.[Yang], Zha, Z.J.[Zheng-Jun],
Co-occurrent Structural Edge Detection for Color-Guided Depth Map Super-Resolution,
MMMod18(I:93-105).
Springer DOI 1802
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Peng, S., Haefner, B., Quéau, Y., Cremers, D.,
Depth Super-Resolution Meets Uncalibrated Photometric Stereo,
CVPV17(2961-2968)
IEEE DOI 1802
Harmonic analysis, Image resolution, Lighting, Mathematical model, Shape, Signal resolution, Standards BibRef

Shiba, Y., Ono, S., Furukawa, R., Hiura, S., Kawasaki, H.,
Temporal Shape Super-Resolution by Intra-frame Motion Encoding Using High-fps Structured Light,
ICCV17(115-123)
IEEE DOI 1802
calibration, cameras, image motion analysis, image reconstruction, image resolution, image sensors, image sequences, BibRef

Gu, S., Zuo, W., Guo, S., Chen, Y., Chen, C., Zhang, L.,
Learning Dynamic Guidance for Depth Image Enhancement,
CVPR17(712-721)
IEEE DOI 1711
Analytical models, Computational modeling, Image enhancement, Image resolution, Sensors, Training, data BibRef

Mieloch, D., Dziembowski, A., Grzelka, A., Stankiewicz, O., Domanski, M.,
Temporal enhancement of graph-based depth estimation method,
WSSIP17(1-4)
IEEE DOI 1707
Cameras, Estimation, Image processing, Motion segmentation, Optimization, Transform coding, Depth estimation, Image segmentation, Temporal, consistency BibRef

Konno, Y., Tanaka, M., Okutomi, M., Yanagawa, Y., Kinoshita, K., Kawade, M.,
Depth map upsampling by self-guided residual interpolation,
ICPR16(1394-1399)
IEEE DOI 1705
Algorithm design and analysis, Art, Estimation, Image resolution, Indexes, Interpolation, Sensors BibRef

Song, X., Huang, H., Zhong, F., Ma, X., Qin, X.,
Edge-guided depth map enhancement,
ICPR16(2758-2763)
IEEE DOI 1705
Color, Image color analysis, Image edge detection, Noise measurement, Optimization, Sensors, Tensile, stress BibRef

Song, X.B.[Xi-Bin], Dai, Y.C.[Yu-Chao], Qin, X.Y.[Xue-Ying],
Deep Depth Super-Resolution: Learning Depth Super-Resolution Using Deep Convolutional Neural Network,
ACCV16(IV: 360-376).
Springer DOI 1704
BibRef

Ye, X.C.[Xin-Chen], Song, X.L.[Xiao-Lin], Yang, J.Y.[Jing-Yu], Hou, C.P.[Chun-Ping], Wang, Y.[Yao],
Depth recovery via decomposition of polynomial and piece-wise constant signals,
VCIP16(1-4)
IEEE DOI 1701
Color BibRef

Zhang, H.T., Kang, K., Wang, Z.F.,
Image guided depth map superresolution using non-local total generalized variation,
VCIP16(1-4)
IEEE DOI 1701
Cameras BibRef

Fu, M., Zhou, W.,
Depth map super-resolution via extended weighted mode filtering,
VCIP16(1-4)
IEEE DOI 1701
Histograms BibRef

Dong, Y., Lin, C., Zhao, Y., Yao, C., Hou, J.,
Depth map up-sampling with texture edge feature via sparse representation,
VCIP16(1-4)
IEEE DOI 1701
Color BibRef

Schneider, N.[Nick], Schneider, L.[Lukas], Pinggera, P.[Peter], Franke, U.[Uwe], Pollefeys, M.[Marc], Stiller, C.[Christoph],
Semantically Guided Depth Upsampling,
GCPR16(37-48).
Springer DOI 1611
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Akcay, O., Erenoglu, R.C., Erenoglu, O.,
Correction and Densification of UAS-Based Photogrammetric Thermal Point Cloud,
ISPRS16(B3: 163-166).
DOI Link 1610
BibRef

Fukushima, N., Takeuchi, K., Kojima, A.,
Self-similarity matching with predictive linear upsampling for depth map,
3DTV-CON16(1-4)
IEEE DOI 1610
edge detection BibRef

Uruma, K., Konishi, K., Takahashi, T., Furukawa, T.,
High resolution depth image recovery algorithm based on the modeling of the sum of an average distance image and a surface image,
ICIP16(2836-2840)
IEEE DOI 1610
Cameras BibRef

Krishnamurthy, S., Ramakrishnan, K.R.,
Image-guided depth map upsampling using normalized cuts-based segmentation and smoothness priors,
ICIP16(554-558)
IEEE DOI 1610
Color BibRef

Liu, W., Chen, X., Yang, J., Wu, Q.,
Robust weighted least squares for guided depth upsampling,
ICIP16(559-563)
IEEE DOI 1610
Color BibRef

Ferstl, D.[David], Rother, M., Bischof, H.,
Variational Depth Superresolution Using Example-Based Edge Representations,
ICCV15(513-521)
IEEE DOI 1602
Dictionaries BibRef

Riegler, G.[Gernot], Ferstl, D.[David], Rüther, M.[Matthias], Bischof, H.[Horst],
A Deep Primal-Dual Network for Guided Depth Super-Resolution,
BMVC16(xx-yy).
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Riegler, G.[Gernot], Rüther, M.[Matthias], Bischof, H.[Horst],
ATGV-Net: Accurate Depth Super-Resolution,
ECCV16(III: 268-284).
Springer DOI 1611
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Riegler, G.[Gernot], Ranftl, R.[René], Rüther, M.[Matthias], Pock, T.[Thomas], Bischof, H.[Horst],
Depth Restoration via Joint Training of a Global Regression Model and CNNs,
BMVC15(xx-yy).
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Denoising and upscaling of depth maps. BibRef

Kim, Y.J.[Young-Jung], Choi, S.[Sunghwan], Oh, C.[Changjae], Sohn, K.H.[Kwang-Hoon],
A majorize-minimize approach for high-quality depth upsampling,
ICIP15(392-396)
IEEE DOI 1512
Depth map upsampling BibRef

Deng, X.W.[Xiao-Wei], Wu, X.L.[Xiao-Lin],
Sparsity-based depth image restoration using surface priors and RGB-D correlations,
ICIP15(3881-3885)
IEEE DOI 1512
Depth image, image restoration, sparsity, superresolution BibRef

Zuo, Y.F.[Yi-Fan], An, P.[Ping], Zheng, S.[Shuai], Zhang, Z.Y.[Zhao-Yang],
Depth upsampling method via Markov random fields without edge-misaligned artifacts,
ICIP15(2324-2328)
IEEE DOI 1512
Markov Random Field (MRF), depth map upsampling, depth recovery BibRef

Schedl, D.C., Birklbauer, C., Bimber, O.,
Directional Super-Resolution by Means of Coded Sampling and Guided Upsampling,
ICCP15(1-10)
IEEE DOI 1511
cameras BibRef

Lu, J.J.[Jia-Jun], Forsyth, D.A.[David A.],
Sparse depth super resolution,
CVPR15(2245-2253)
IEEE DOI 1510
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Kwon, H.[Hyeok_Hyen], Tai, Y.W.[Yu-Wing], Lin, S.[Stephen],
Data-driven depth map refinement via multi-scale sparse representation,
CVPR15(159-167)
IEEE DOI 1510
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Herrera, J.L., del-Blanco, C.R., Garcia, N.,
Edge-based depth gradient refinement for 2D to 3D learned prior conversion,
3DTV-CON15(1-4)
IEEE DOI 1508
Clustering algorithms BibRef

Schoenenberger, Y., Paratte, J., Vandergheynst, P.,
Graph-based denoising for time-varying point clouds,
3DTV-CON15(1-4)
IEEE DOI 1508
Manifolds BibRef

Lee, G.G.C., Li, B.S.[Bo-Syun], Chen, C.F.[Chun-Fu],
Content-adaptive depth map enhancement based on motion distribution,
VCIP14(482-485)
IEEE DOI 1504
filtering theory BibRef

Joachimiak, M.[Michal], Aflaki, P.[Payman], Hannuksela, M.M.[Miska M.], Gabbouj, M.[Moncef],
Evaluation of Depth-Based Super Resolution on Compressed Mixed Resolution 3D Video,
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Vianello, A., Michielin, F., Calvagno, G., Sartor, P., Erdler, O.,
Depth images super-resolution: An iterative approach,
ICIP14(3778-3782)
IEEE DOI 1502
Cameras;Color;Noise;Spatial resolution;Standards;Stereo vision BibRef

dos Santos, L.T.A.[Leandro Tavares Aragão], Loaiza Fernandez, M.E.[Manuel Eduardo], Raposo, A.B.[Alberto Barbosa],
Generating Super-Resolved Depth Maps Using Low-Cost Sensors and RGB Images,
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Li, L.[Li], Zhang, C.M.[Cai-Ming],
A Nonlocal Filter-Based Hybrid Strategy for Depth Map Enhancement,
ICPR14(4394-4399)
IEEE DOI 1412
Color BibRef

Wang, Y.C.[Yu-Cheng], Di, H.J.[Hui-Jun], Wang, B.J.[Bing-Jie], Liang, W.[Wei], Zhang, J.[Jian], Jia, Y.D.[Yun-De],
Depth Super-resolution by Fusing Depth Imaging and Stereo Vision with Structural Determinant Information Inference,
ICPR14(4212-4217)
IEEE DOI 1412
Art BibRef

Ghesu, F.C.[Florin C.], Köhler, T.[Thomas], Haase, S.[Sven], Hornegger, J.[Joachim],
Guided Image Super-Resolution: A New Technique for Photogeometric Super-Resolution in Hybrid 3-D Range Imaging,
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BibRef

Hui, T.W.[Tak-Wai], Ngan, K.N.[King Ngi],
Motion-Depth: RGB-D Depth Map Enhancement with Motion and Depth in Complement,
CVPR14(3962-3969)
IEEE DOI 1409
BibRef
And:
Dense depth map generation using sparse depth data from normal flow,
ICIP14(3837-3841)
IEEE DOI 1502
BibRef
And:
Depth enhancement using RGB-D guided filtering,
ICIP14(3832-3836)
IEEE DOI 1502
Cameras. Approximation methods BibRef

Rana, P.K., Taghia, J., Flierl, M.,
Statistical methods for inter-view depth enhancement,
3DTV-CON14(1-4)
IEEE DOI 1409
image enhancement BibRef

Gong, X.J.[Xiao-Jin], Ren, J.Q.[Jian-Qiang], Lai, B.S.[Bai-Sheng], Yan, C.H.[Chao-Hua], Qian, H.[Hui],
Guided Depth Upsampling via a Cosparse Analysis Model,
FusionOutdoor14(738-745)
IEEE DOI 1409
Guided depth upsampling BibRef

Correia, P., Marcelino, S., Assuncao, P., Faria, S., Soares, S., Pagliari, C., da Silva, E.,
Enhancement method for multiple description decoding of depth maps subject to random loss,
3DTV-CON14(1-4)
IEEE DOI 1409
decoding BibRef

Li, J.[Jing], Lu, Z.C.[Zhi-Chao], Zeng, G.[Gang], Gan, R.[Rui], Zha, H.B.[Hong-Bin],
Similarity-Aware Patchwork Assembly for Depth Image Super-resolution,
CVPR14(3374-3381)
IEEE DOI 1409
Assembly, Disassemble, Dpeth map super resolution, Self-similarity BibRef

Joachimiak, M., Hannuksela, M.M., Gabbouj, M.,
View synthesis quality mapping for depth-based super resolution on mixed resolution 3D video,
3DTV-CON14(1-4)
IEEE DOI 1409
image resolution BibRef

Dai, L.Q.[Long-Quan], Wang, H.X.[Hao-Xing], Mei, X.[Xing], Zhang, X.P.[Xiao-Peng],
Depth Map Upsampling via Compressive Sensing,
ACPR13(90-94)
IEEE DOI 1408
compressed sensing BibRef

Ferstl, D.[David], Reinbacher, C.[Christian], Ranftl, R.[Rene], Ruether, M.[Matthias], Bischof, H.[Horst],
Image Guided Depth Upsampling Using Anisotropic Total Generalized Variation,
ICCV13(993-1000)
IEEE DOI 1403
anisotropic tensor BibRef

Silva, J.W.[Jong Wan], Gomes, L.[Leonardo], Aguero, K.A.[Karl Apaza], Bellon, O.R.P.[Olga R.P.], Silva, L.[Luciano],
Real-time acquisition and super-resolution techniques on 3D reconstruction,
ICIP13(2135-2139)
IEEE DOI 1402
3D reconstruction;real-time;super-resolution BibRef

Zheng, H., Bouzerdoum, A., Phung, S.L.,
Depth image super-resolution using multi-dictionary sparse representation,
ICIP13(957-961)
IEEE DOI 1402
Cameras BibRef

Davoodianidaliki, M., Saadatseresht, M.,
Three Pre-Processing Steps to Increase the Quality of Kinect Range Data,
SMPR13(127-132).
DOI Link 1311
BibRef

Ismaeil, K.A.[Kassem Al], Aouada, D.[Djamila],
Depth Super-Resolution by Enhanced Shift and Add,
CAIP13(II:100-107).
Springer DOI 1311
BibRef

Liu, M.Y.[Ming-Yu], Tuzel, O.[Oncel], Taguchi, Y.[Yuichi],
Joint Geodesic Upsampling of Depth Images,
CVPR13(169-176)
IEEE DOI 1309
depth, filtering, geodesic, upsampling BibRef

Yu, L.F.[Lap-Fai], Yeung, S.K.[Sai-Kit], Tai, Y.W.[Yu-Wing], Lin, S.[Stephen],
Shading-Based Shape Refinement of RGB-D Images,
CVPR13(1415-1422)
IEEE DOI 1309
BibRef

Hornacek, M.[Michael], Rhemann, C.[Christoph], Gelautz, M.[Margrit], Rother, C.[Carsten],
Depth Super Resolution by Rigid Body Self-Similarity in 3D,
CVPR13(1123-1130)
IEEE DOI 1309
dense matching, depth super resolution, optimization BibRef

Kim, J., Lee, J.K., Lee, K.M.,
Deeply-Recursive Convolutional Network for Image Super-Resolution,
CVPR16(1637-1645)
IEEE DOI 1612
BibRef

Hong, C.[Cheeun], Kim, H.[Heewon], Baik, S.[Sungyong], Oh, J.[Junghun], Lee, K.M.[Kyoung Mu],
DAQ: Channel-Wise Distribution-Aware Quantization for Deep Image Super-Resolution Networks,
WACV22(913-922)
IEEE DOI 2202
Training, Deep learning, Quantization (signal), Costs, Data acquisition, Superresolution, Image Processing -> Image Restoration Deep Learning -> Efficient Training and Inference Methods for Networks BibRef

Lim, B.[Bee], Son, S.[Sanghyun], Kim, H.[Heewon], Nah, S.[Seungjun], Lee, K.M.[Kyoung Mu],
Enhanced Deep Residual Networks for Single Image Super-Resolution,
NTIRE17(1132-1140)
IEEE DOI 1709
Computational modeling, Computer architecture, Convolution, Image reconstruction, Image resolution, Signal resolution, Training BibRef

Kim, J., Lee, J.K., Lee, K.M.,
Accurate Image Super-Resolution Using Very Deep Convolutional Networks,
CVPR16(1646-1654)
IEEE DOI 1612
BibRef

Lee, H.S.[Hee Seok], Lee, K.M.[Kuoung Mu],
Dense 3D Reconstruction from Severely Blurred Images Using a Single Moving Camera,
CVPR13(273-280)
IEEE DOI 1309
Dense 3D reconstruction, Image deblurring, Visual SLAM BibRef

Lee, H.S.[Hee Seok], Lee, K.M.[Kuoung Mu],
Simultaneous Super-Resolution of Depth and Images Using a Single Camera,
CVPR13(281-288)
IEEE DOI 1309
Dense 3D reconstruction, Image super-resolution, Visual SLAM
See also Simultaneous Super-Resolution of Depth and Images Using a Single Camera. BibRef

Nelson, K., Bhatti, A., Nahavandi, S.,
Super-resolution of a 3-dimensional scene from novel viewpoints,
ICARCV12(1380-1385).
IEEE DOI 1304
BibRef

Li, J.[Jing], Lu, Z.C.[Zhi-Chao], Zeng, G.[Gang], Gan, R.[Rui], Wang, L.[Long], Zha, H.B.[Hong-Bin],
A Joint Learning-Based Method for Multi-view Depth Map Super Resolution,
ACPR13(456-460)
IEEE DOI 1408
BibRef
Earlier: A1, A3, A4, A6, A5, Only:
A Bayesian Approach to Uncertainty-Based Depth Map Super Resolution,
ACCV12(IV:205-216).
Springer DOI 1304
image colour analysis BibRef

Schwarz, S.[Sebastian], Sjostrom, M.[Marten], Olsson, R.[Roger],
Incremental depth upscaling using an edge weighted optimization concept,
3DTV12(1-4).
IEEE DOI 1212
BibRef

Aodha, O.M.[Oisin Mac], Campbell, N.D.F.[Neill D. F.], Nair, A.[Arun], Brostow, G.J.[Gabriel J.],
Patch Based Synthesis for Single Depth Image Super-Resolution,
ECCV12(III: 71-84).
Springer DOI 1210
BibRef

Gevrekci, M.[Murat], Pakin, K.[Kubilay],
Depth map super resolution,
ICIP11(3449-3452).
IEEE DOI 1201
BibRef

Edeler, T., Ohliger, K., Hussmann, S., Mertins, A.,
Super resolution of time-of-flight depth images under consideration of spatially varying noise variance,
ICIP09(1185-1188).
IEEE DOI 0911
BibRef

Awatsu, Y.[Yusaku], Kawai, N.[Norihiko], Sato, T.[Tomokazu], Yokoya, N.[Naokazu],
Spatio-temporal Super-Resolution Using Depth Map,
SCIA09(696-705).
Springer DOI 0906
BibRef

Li, F.[Feng], Yu, J.Y.[Jing-Yi], Chai, J.X.[Jin-Xiang],
A hybrid camera for motion deblurring and depth map super-resolution,
CVPR08(1-8).
IEEE DOI 0806
BibRef

Yang, Q.X.[Qing-Xiong], Yang, R.G.[Rui-Gang], Davis, J.W.[James W.], Nister, D.[David],
Spatial-Depth Super Resolution for Range Images,
CVPR07(1-8).
IEEE DOI 0706
BibRef

Zhang, S.[Song], Royer, D.[Dale], Yau, S.T.[Shing-Tung],
High-resolution, real-time-geometry video acquisition,
SigGraph06(Article 110).
WWW Link. BibRef 0600

Zhang, S.[Song], Huang, P.S.[Pei-Sen],
High-Resolution, Real-time 3D Shape Acquisition,
Sensor3D04(28).
IEEE DOI 0406
BibRef

Zhang, S.[Song],
High-Resolution, Real-Time 3-D Shape Measurement,
Ph.D.Thesis, 2005, State University of New York, Stony Brook. BibRef 0500

Chapter on Motion Analysis -- Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion continues in
Creating Super Resolution Image from Video .

Last update:May 22, 2023 at 22:32:27