Chen, L.[Li],
Tian, J.[Jing],
Depth image enlargement using an evolutionary approach,
SP:IC(28), No. 7, 2013, pp. 745-752.
Elsevier DOI
1307
BibRef
Earlier: A2, A1:
Depth image up-sampling using ant colony optimization,
ICPR12(3795-3798).
WWW Link.
1302
BibRef
And: A2, A1:
Bayesian image enlargement for mixed-resolution video,
ICPR12(3082-3085).
WWW Link.
1302
BibRef
Earlier: A2, A1:
Bayesian stereoscopic image resolution enhancement,
ICIP11(1505-1508).
IEEE DOI
1201
BibRef
Earlier: A2, A1:
Multi-focus image fusion using wavelet-domain statistics,
ICIP10(1205-1208).
IEEE DOI
1009
Depth map
See also Fusing remote sensing images using a trous wavelet transform and empirical mode decomposition.
BibRef
Richter, T.[Thomas],
Seiler, J.[Jurgen],
Schnurrer, W.[Wolfgang],
Kaup, A.[Andre],
Robust Super-Resolution for Mixed-Resolution Multiview Image Plus
Depth Data,
CirSysVideo(26), No. 5, May 2016, pp. 814-828.
IEEE DOI
1605
BibRef
Earlier:
Super-resolution for mixed-resolution multiview image plus depth data
using a novel two-stage high-frequency extrapolation method for
occluded areas,
ICIP15(1880-1884)
IEEE DOI
1512
Mixed-Resolution, Signal Extrapolation, Stereo Imaging, Super-Resolution
BibRef
Seiler, J.,
Jonscher, M.,
Ussmueller, T.,
Kaup, A.,
Increasing Imaging Resolution by Non-Regular Sampling and Joint
Sparse Deconvolution and Extrapolation,
CirSysVideo(29), No. 2, February 2019, pp. 308-322.
IEEE DOI
1902
Image sensors, Sensors, Image resolution, Image reconstruction,
Layout, Reconstruction algorithms, Prototypes,
spare reconstruction
BibRef
Richter, T.[Thomas],
Kaup, A.[Andre],
Multiview super-resolution using high-frequency synthesis in case of
low-framerate depth information,
VCIP12(1-6).
IEEE DOI
1302
BibRef
Richter, T.,
Habermann, A.,
Kaup, A.,
Super-resolution for mixed-resolution multiview images using a
relative frequency response estimation method,
VCIP15(1-4)
IEEE DOI
1605
Cameras
BibRef
Genser, N.,
Seiler, J.[Jurgen],
Jonscher, M.[Markus],
Kaup, A.,
Demonstration of rapid frequency selective reconstruction for image
resolution enhancement,
ICIP17(4595-4595)
IEEE DOI
1803
Computers, Image reconstruction, Image resolution, Image sensors, Sensors
BibRef
Schnurrer, W.[Wolfgang],
Jonscher, M.[Markus],
Seiler, J.[Jurgen],
Richter, T.[Thomas],
Batz, M.,
Kaup, A.[Andre],
Centroid adapted frequency selective extrapolation for reconstruction
of lost image areas,
VCIP15(1-4)
IEEE DOI
1605
Adaptation models
See also Reducing randomness of non-regular sampling masks for image reconstruction.
BibRef
Lei, J.J.[Jian-Jun],
Zhang, Z.[Zhe],
Fan, X.T.[Xiao-Ting],
Yang, B.[Bolan],
Li, X.X.[Xin-Xin],
Chen, Y.[Ying],
Huang, Q.M.[Qing-Ming],
Deep Stereoscopic Image Super-Resolution via Interaction Module,
CirSysVideo(31), No. 8, August 2021, pp. 3051-3061.
IEEE DOI
2108
Stereo image processing, Image reconstruction,
Feature extraction, Correlation, Spatial resolution,
deep learning
BibRef
Zhang, Z.[Zhe],
Peng, B.[Bo],
Lei, J.J.[Jian-Jun],
Shen, H.F.[Hai-Feng],
Huang, Q.M.[Qing-Ming],
Recurrent Interaction Network for Stereoscopic Image Super-Resolution,
CirSysVideo(33), No. 5, May 2023, pp. 2048-2060.
IEEE DOI
2305
Stereo image processing, Superresolution, Image reconstruction,
Feature extraction, Spatial resolution, Iterative methods,
feature interaction
BibRef
Ning, L.[Luyao],
Wang, A.H.[An-Hong],
Zhao, L.J.[Li-Jun],
Xue, W.M.[Wei-Min],
Bu, D.H.[Dong-Han],
MRANet: Multi-atrous residual attention Network for stereo image
super-resolution,
JVCIR(77), 2021, pp. 103115.
Elsevier DOI
2106
Stereo cameras, Stereo image super-resolution,
Discriminative ability, Parallax extraction, Attention mechanism
BibRef
Dan, J.W.[Jia-Wang],
Qu, Z.W.[Zhao-Wei],
Wang, X.R.[Xiao-Ru],
Gu, J.A.[Ji-Ahang],
A Disparity Feature Alignment Module for Stereo Image
Super-Resolution,
SPLetters(28), 2021, pp. 1285-1289.
IEEE DOI
2107
Convolution, Superresolution, Training, Spatial resolution,
Memory management, Graphics processing units, Training data,
attention mechanism
BibRef
Duan, C.Y.[Chen-Yang],
Xiao, N.F.[Nan-Feng],
Parallax-based second-order mixed attention for stereo image
super-resolution,
IET-CV(16), No. 1, 2022, pp. 26-37.
DOI Link
2202
BibRef
Chen, C.Q.[Can-Qiang],
Qing, C.M.[Chun-Mei],
Xu, X.M.[Xiang-Min],
Dickinson, P.[Patrick],
Cross Parallax Attention Network for Stereo Image Super-Resolution,
MultMed(24), 2022, pp. 202-216.
IEEE DOI
2202
Task analysis, Superresolution,
Cameras, Visualization, Spatial resolution, Estimation,
convolutional neural network
BibRef
Zhu, X.Y.[Xiang-Yuan],
Guo, K.[Kehua],
Fang, H.[Hui],
Chen, L.[Liang],
Ren, S.[Sheng],
Hu, B.[Bin],
Cross View Capture for Stereo Image Super-Resolution,
MultMed(24), 2022, pp. 3074-3086.
IEEE DOI
2206
Superresolution, Feature extraction, Image reconstruction,
Spatial resolution, Task analysis, Visual perception, Training,
spatial perception
BibRef
Jin, K.J.[Kang-Jun],
Wang, X.[Xuejin],
Shao, F.[Feng],
Jointly Texture Enhanced and Stereo Captured Network for Stereo Image
Super-Resolution,
PRL(167), 2023, pp. 141-148.
Elsevier DOI
2303
texture attention, parallax attention, stereo image super-resolution
BibRef
Chu, X.J.[Xiao-Jie],
Chen, L.[Liangyu],
Yu, W.Q.[Wen-Qing],
NAFSSR: Stereo Image Super-Resolution Using NAFNet,
NTIRE22(1238-1247)
IEEE DOI
2210
Training, Fuses, Computational modeling,
Superresolution, Stochastic processes, Feature extraction
BibRef
Wang, L.G.[Long-Guang],
Guo, Y.L.[Yu-Lan],
Wang, Y.Q.[Ying-Qian],
Li, J.C.[Jun-Cheng],
Gu, S.H.[Shu-Hang],
Timofte, R.[Radu],
Chen, L.Y.[Liang-Yu],
Chu, X.J.[Xiao-Jie],
Yu, W.Q.[Wen-Qing],
Jin, K.[Kai],
Wei, Z.Q.[Ze-Qiang],
Guo, S.[Sha],
Yang, A.[Angulia],
Zhou, X.Z.[Xiu-Zhuang],
Guo, G.D.[Guo-Dong],
Dai, B.[Bin],
Peng, F.Y.[Fei-Yue],
Xiao, H.X.[Hua-Xin],
Yan, S.[Shen],
Liu, Y.X.[Yu-Xiang],
Cai, H.X.[Han-Xiao],
Cao, P.[Pu],
Nie, Y.[Yang],
Yang, L.[Lu],
Song, Q.[Qing],
Hu, X.T.[Xiao-Tao],
Xu, J.[Jun],
Xu, M.[Mai],
Jing, J.P.[Jun-Peng],
Deng, X.[Xin],
Xing, Q.[Qunliang],
Qiao, M.L.[Ming-Lang],
Guan, Z.Y.[Zhen-Yu],
Guo, W.L.[Wen-Long],
Peng, C.X.[Chen-Xu],
Chen, Z.[Zan],
Chen, J.Y.[Jun-Yang],
Li, H.[Hao],
Chen, J.B.[Jun-Bin],
Li, W.J.[Wei-Jie],
Yang, Z.J.[Zhi-Jing],
Li, G.[Gen],
Li, A.[Aijin],
Sun, L.[Lei],
Zhang, D.[Dafeng],
Liu, S.[Shizhuo],
Zhang, J.T.[Jiang-Tao],
Qu, Y.[Yanyun],
Yang, H.H.[Hao-Hsiang],
Huang, Z.K.[Zhi-Kai],
Chen, W.T.[Wei-Ting],
Chang, H.E.[Hua-En],
Kuo, S.Y.[Sy-Yen],
Liang, Q.[Qiaohui],
Lin, J.X.[Jian-Xin],
Wang, Y.J.[Yi-Jun],
Yin, L.[Lianying],
Zhang, R.[Rongju],
Zhao, W.[Wei],
Xiao, P.[Peng],
Xu, R.J.[Rong-Jian],
Zhang, Z.[Zhilu],
Zuo, W.M.[Wang-Meng],
Guo, H.S.[Han-Sheng],
Gao, G.[Guangwei],
Zeng, T.Y.[Tie-Yong],
Pi, H.[Huicheng],
Zhang, S.[Shunli],
Kim, J.[Joohyeok],
Kim, H.[HyeonA],
Park, E.[Eunpil],
Sim, J.Y.[Jae-Young],
Zhai, J.[Jucai],
Zeng, P.C.[Peng-Cheng],
Liu, Y.[Yang],
Ma, C.[Chihao],
Huang, Y.L.[Yu-Lin],
Chen, J.[Junying],
NTIRE 2022 Challenge on Stereo Image Super-Resolution: Methods and
Results,
NTIRE22(905-918)
IEEE DOI
2210
Degradation, Superresolution, Benchmark testing,
Pattern recognition, Image restoration
BibRef
Ma, L.[Li],
Li, S.[Sumei],
Enhanced Back Projection Network Based Stereo Image Super-Resolution
Considering Parallax Attention,
ICIP21(1834-1838)
IEEE DOI
2201
Superresolution, Estimation, Benchmark testing, Feature extraction,
Task analysis, Image reconstruction,
back projection network
BibRef
Tosi, F.[Fabio],
Liao, Y.[Yiyi],
Schmitt, C.[Carolin],
Geiger, A.[Andreas],
SMD-Nets: Stereo Mixture Density Networks,
CVPR21(8938-8948)
IEEE DOI
2111
Solid modeling, Uncertainty,
Superresolution, Estimation, Pattern recognition, Spatial resolution
BibRef
Gee, T.[Trevor],
Gimel’farb, G.[Georgy],
Woodward, A.[Alexander],
Ababou, R.[Rachel],
Strozzi, A.G.[Alfonso Gastelum],
Delmas, P.[Patrice],
Guided Stereo to Improve Depth Resolution of a Small Baseline Stereo
Camera Using an Image Sequence,
ACIVS20(480-491).
Springer DOI
2003
BibRef
Li, W.[Weifu],
John, V.[Vijay],
Mita, S.[Seiichi],
Enhancing Depth Quality of Stereo Vision using Deep Learning-based
Prior Information of the Driving Environment,
ICPR21(7281-7286)
IEEE DOI
2105
Deep learning, Image segmentation, Roads,
Graphics processing units, Real-time systems, Stereo vision, Pattern recognition
BibRef
Jeon, D.S.,
Baek, S.,
Choi, I.,
Kim, M.H.,
Enhancing the Spatial Resolution of Stereo Images Using a Parallax
Prior,
CVPR18(1721-1730)
IEEE DOI
1812
Spatial resolution, Image reconstruction, Imaging, Color,
Image color analysis, Training
BibRef
Li, J.,
You, S.,
Robles-Kelly, A.,
Stereo Super-Resolution via a Deep Convolutional Network,
DICTA17(1-7)
IEEE DOI
1804
convolution, image resolution,
learning (artificial intelligence), neural nets,
Training
BibRef
Kimura, K.[Kazuto],
Nagai, T.[Takayuki],
Nagayoshi, H.[Hiroto],
Sako, H.[Hiroshi],
Simultaneous Estimation of Super-Resolved Image and 3D Information
using Multiple Stereo-Pair Images,
ICIP07(V: 417-420).
IEEE DOI
0709
BibRef
Or, S.H.[Siu Hang],
Yu, Y.K.[Ying Kin],
Wong, K.H.[Kin Hong],
Chang, M.M.Y.,
Resolution Improvement from Stereo Images with 3D Pose Differences,
ICIP06(1733-1736).
IEEE DOI
0610
BibRef
And: A2, A3, A4, A1:
Computing Pose Sequences Directly from Videos,
ICIP06(2773-2776).
IEEE DOI
0610
BibRef
Mitra, K.[Kaushik],
Veeraraghavan, A.[Ashok],
Light field denoising, light field superresolution and stereo camera
based refocussing using a GMM light field patch prior,
CCD12(22-28).
IEEE DOI
1207
BibRef
Chapter on Motion Analysis -- Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion continues in
Range Data Super Resolution, Depth Super Resolution .