19.4.3.5 Super Resolution for Light Field Images and Data

Chapter Contents (Back)
Super Resolution. Light Field.

Bishop, T.E.[Tom E.], Favaro, P.[Paolo],
The Light Field Camera: Extended Depth of Field, Aliasing, and Superresolution,
PAMI(34), No. 5, May 2012, pp. 972-986.
IEEE DOI 1204
BibRef
Earlier:
Full-Resolution Depth Map Estimation from an Aliased Plenoptic Light Field,
ACCV10(II: 186-200).
Springer DOI 1011
BibRef
Earlier:
Plenoptic depth estimation from multiple aliased views,
3DIM09(1622-1629).
IEEE DOI 0910
Several low resolution views to infer depth.
See also Geometric Approach to Shape from Defocus, A. BibRef

Wanner, S.[Sven], Goldluecke, B.[Bastian],
Variational Light Field Analysis for Disparity Estimation and Super-Resolution,
PAMI(36), No. 3, March 2014, pp. 606-619.
IEEE DOI 1403
BibRef
Earlier: A2, A1:
The Variational Structure of Disparity and Regularization of 4D Light Fields,
CVPR13(1003-1010)
IEEE DOI 1309
BibRef
Earlier: A1, A2:
Globally consistent depth labeling of 4D light fields,
CVPR12(41-48).
IEEE DOI 1208
BibRef
And: A1, A2:
Spatial and Angular Variational Super-Resolution of 4D Light Fields,
ECCV12(V: 608-621).
Springer DOI 1210
inverse problems, light field analysis, variational methods convex programming. BibRef

Krolla, B.[Bernd], Diebold, M.[Maximilian], Stricker, D.[Didier],
Light Field from Smartphone-Based Dual Video,
LightField14(600-610).
Springer DOI 1504
BibRef

Krolla, B.[Bernd], Diebold, M.[Maximilian], Goldluecke, B.[Bastian], Stricker, D.[Didier],
Spherical Light Fields,
BMVC14(xx-yy).
HTML Version. 1410
BibRef

Wanner, S.[Sven], Straehle, C.[Christoph], Goldluecke, B.[Bastian],
Globally Consistent Multi-label Assignment on the Ray Space of 4D Light Fields,
CVPR13(1011-1018)
IEEE DOI 1309
light field analysis, multi-label problems, variational methods BibRef

Strecke, M.[Michael], Alperovich, A.[Anna], Goldluecke, B.[Bastian],
Accurate Depth and Normal Maps from Occlusion-Aware Focal Stack Symmetry,
CVPR17(2529-2537)
IEEE DOI 1711
Algorithm design and analysis, Benchmark testing, Cameras, Cost function, Estimation, Robustness BibRef

Yoon, Y., Jeon, H.G., Yoo, D., Lee, J.Y.[Joon-Young], Kweon, I.S.[In So],
Light-Field Image Super-Resolution Using Convolutional Neural Network,
SPLetters(24), No. 6, June 2017, pp. 848-852.
IEEE DOI 1705
BibRef
Earlier:
Learning a Deep Convolutional Network for Light-Field Image Super-Resolution,
CVPV15(57-65)
IEEE DOI 1602
Cameras, Convolution, Neural networks, Signal resolution, Spatial resolution, Training, Convolutional neural network, light-field (LF) image super-resolution (SR). Cameras, Image restoration, Lenses, Neural networks, Spatial resolution BibRef

Rossi, M., Frossard, P.,
Geometry-Consistent Light Field Super-Resolution via Graph-Based Regularization,
IP(27), No. 9, September 2018, pp. 4207-4218.
IEEE DOI 1807
cameras, geometry, graph theory, image reconstruction, image resolution, rendering (computer graphics), 3D information, super-resolution BibRef

Rossi, M., Gheche, M.E., Frossard, P.,
A Nonsmooth Graph-Based Approach to Light Field Super-Resolution,
ICIP18(2590-2594)
IEEE DOI 1809
Spatial resolution, Signal resolution, Cameras, Estimation, Optimization, light field, super-resolution, graph BibRef

Wang, Y.L.[Yun-Long], Liu, F.[Fei], Zhang, K.B.[Kun-Bo], Hou, G.Q.[Guang-Qi], Sun, Z.N.[Zhe-Nan], Tan, T.N.[Tie-Niu],
LFNet: A Novel Bidirectional Recurrent Convolutional Neural Network for Light-Field Image Super-Resolution,
IP(27), No. 9, September 2018, pp. 4274-4286.
IEEE DOI 1807
convolution, feedforward neural nets, image fusion, image reconstruction, image resolution, recurrent neural nets, super-resolution BibRef

Mukati, M.U.[M. Umair], Gunturk, B.K.[Bahadir K.],
Light field super resolution through controlled micro-shifts of light field sensor,
SP:IC(67), 2018, pp. 71-78.
Elsevier DOI 1808
Light field, Super-resolution, Micro-scanning BibRef

Yuan, Y., Cao, Z., Su, L.,
Light-Field Image Superresolution Using a Combined Deep CNN Based on EPI,
SPLetters(25), No. 9, September 2018, pp. 1359-1363.
IEEE DOI 1809
cameras, convolution, feedforward neural nets, image enhancement, image resolution, light-field image superresolution, superresolution (SR) BibRef

Yeung, H.W.F.[Henry Wing Fung], Hou, J.H.[Jun-Hui], Chen, X.M.[Xiao-Ming], Chen, J.[Jie], Chen, Z.B.[Zhi-Bo], Chung, Y.Y.[Yuk Ying],
Light Field Spatial Super-Resolution Using Deep Efficient Spatial-Angular Separable Convolution,
IP(28), No. 5, May 2019, pp. 2319-2330.
IEEE DOI 1903
cameras, convolution, convolutional neural nets, feature extraction, image reconstruction, image resolution, convolutional neural networks BibRef

Guo, M.[Mantang], Hou, J.H.[Jun-Hui], Jin, J.[Jing], Chen, J.[Jie], Chau, L.P.[Lap-Pui],
Deep Spatial-Angular Regularization for Light Field Imaging, Denoising, and Super-Resolution,
PAMI(44), No. 10, October 2022, pp. 6094-6110.
IEEE DOI 2209
Image reconstruction, Apertures, Noise reduction, Cameras, Sensors, Reconstruction algorithms, Imaging, Light field, coded aperture, depth BibRef

Ghassab, V.K., Bouguila, N.,
Light Field Super-Resolution Using Edge-Preserved Graph-Based Regularization,
MultMed(22), No. 6, June 2020, pp. 1447-1457.
IEEE DOI 2005
Cameras, Image edge detection, Image reconstruction, Spatial resolution, Light field, graph BibRef

Cheng, Z.[Zhen], Xiong, Z.W.[Zhi-Wei], Liu, D.[Dong],
Light Field Super-Resolution By Jointly Exploiting Internal and External Similarities,
CirSysVideo(30), No. 8, August 2020, pp. 2604-2616.
IEEE DOI 2008
Spatial resolution, Cameras, Correlation, Light fields, Two dimensional displays, Light field, super-resolution, deep learning BibRef

Xiong, Z.W.[Zhi-Wei], Cheng, Z.[Zhen], Peng, J.Y.[Jia-Yong], Fan, H.Z.[Han-Zhi], Liu, D.[Dong], Wu, F.[Feng],
Light field super-resolution using internal and external similarities,
ICIP17(1612-1616)
IEEE DOI 1803
Cameras, Correlation, Interpolation, Projection algorithms, Spatial resolution, Light field, depth, super-resolution BibRef

Wang, C.[Chen], Qi, N.[Na], Zhu, Q.[Qing],
Tensor-Based Light Field Denoising By Exploiting Non-Local Similarities Across Multiple Resolutions,
ICIP20(1078-1082)
IEEE DOI 2011
Noise reduction, Tensile stress, Correlation, Signal resolution, Spatial resolution, Noise measurement, Light field, super-resolution BibRef

Liu, Y.[Yun], Qi, N.[Na], Xiong, Z.W.[Zhi-Wei],
Tensor-based plenoptic image denoising by integrating super-resolution,
SP:IC(108), 2022, pp. 116789.
Elsevier DOI 2209
Plenoptic image, Denoising, Super-resolution, Nonlocal similarity, Back-projection BibRef

Liu, Y.[Yun], Qi, N.[Na], Cheng, Z.[Zhen], Liu, D.[Dong], Ling, Q.[Qing], Xiong, Z.W.[Zhi-Wei],
Tensor-Based Light Field Denoising by Integrating Super-Resolution,
ICIP18(3209-3213)
IEEE DOI 1809
Noise reduction, Tensile stress, Spatial resolution, Visualization, Human computer interaction, Light field, denoising, back-projection BibRef

Cheng, Z.[Zhen], Xiong, Z.W.[Zhi-Wei], Chen, C.[Chang], Liu, D.[Dong], Zha, Z.J.[Zheng-Jun],
Light Field Super-Resolution with Zero-Shot Learning,
CVPR21(10005-10014)
IEEE DOI 2111
Deep learning, Adaptation models, Superresolution, Training data, Light fields, Data mining BibRef

Farrugia, R.A.[Reuben A.], Guillemot, C.[Christine],
A simple framework to leverage state-of-the-art single-image super-resolution methods to restore light fields,
SP:IC(80), 2020, pp. 115638.
Elsevier DOI 1912
Light fields, Super-resolution, Convolutional neural networks, Single image super resolution BibRef

Farrugia, R.A.[Reuben A.], Guillemot, C.[Christine],
Light Field Super-Resolution Using a Low-Rank Prior and Deep Convolutional Neural Networks,
PAMI(42), No. 5, May 2020, pp. 1162-1175.
IEEE DOI 2004
Spatial resolution, Cameras, Image restoration, Matrix decomposition, Sparse matrices, Light fields, super-resolution BibRef

Jiang, X.R.[Xiao-Ran], Shi, J.L.[Jing-Lei], Guillemot, C.[Christine],
An Untrained Neural Network Prior for Light Field Compression,
IP(31), 2022, pp. 6922-6936.
IEEE DOI 2212
Image coding, Image reconstruction, Decoding, Adaptation models, Video compression, Predictive models, Rate-distortion, compact representation BibRef

Aquilina, M.[Matthew], Galea, C.[Christian], Abela, J.[John], Camilleri, K.P.[Kenneth P.], Farrugia, R.A.[Reuben A.],
Improving Super-Resolution Performance Using Meta-Attention Layers,
SPLetters(28), 2021, pp. 2082-2086.
IEEE DOI 2112
Metadata, Degradation, Faces, Kernel, Training, Image coding, Signal resolution, Super-resolution, image restoration, metadata fusion BibRef

Wang, Y.Q.[Ying-Qian], Yang, J.G.[Jun-Gang], Wang, L.G.[Long-Guang], Ying, X.Y.[Xin-Yi], Wu, T.H.[Tian-Hao], An, W.[Wei], Guo, Y.L.[Yu-Lan],
Light Field Image Super-Resolution Using Deformable Convolution,
IP(30), 2021, pp. 1057-1071.
IEEE DOI 2012
Light field, super-resolution, deformable convolution, dataset BibRef

Liang, Z.Y.[Zheng-Yu], Wang, Y.Q.[Ying-Qian], Wang, L.G.[Long-Guang], Yang, J.G.[Jun-Gang], Zhou, S.L.[Shi-Lin], Guo, Y.L.[Yu-Lan],
Learning Non-Local Spatial-Angular Correlation for Light Field Image Super-Resolution,
ICCV23(12342-12352)
IEEE DOI Code:
WWW Link. 2401
BibRef

Wang, Y.Q.[Ying-Qian], Wang, L.G.[Long-Guang], Yang, J.G.[Jun-Gang], An, W.[Wei], Yu, J.Y.[Jing-Yi], Guo, Y.L.[Yu-Lan],
Spatial-angular Interaction for Light Field Image Super-resolution,
ECCV20(XXIII:290-308).
Springer DOI 2011
BibRef

Mo, Y.[Yu], Wang, Y.Q.[Ying-Qian], Xiao, C.[Chao], Yang, J.G.[Jun-Gang], An, W.[Wei],
Dense Dual-Attention Network for Light Field Image Super-Resolution,
CirSysVideo(32), No. 7, July 2022, pp. 4431-4443.
IEEE DOI 2207
Spatial resolution, Feature extraction, Superresolution, Convolutional neural networks, Data mining, Correlation, dense connection BibRef

Mo, Y.[Yu], Wang, Y.Q.[Ying-Qian], Wang, L.G.[Long-Guang], Yang, J.G.[Jun-Gang], An, W.[Wei],
Light Field Angular Super-resolution via Dense Correspondence Field Reconstruction,
AIM22(412-428).
Springer DOI 2304
BibRef

Wang, Y.Q.[Ying-Qian], Wang, L.G.[Long-Guang], Wu, G.C.[Gao-Chang], Yang, J.G.[Jun-Gang], An, W.[Wei], Yu, J.Y.[Jing-Yi], Guo, Y.L.[Yu-Lan],
Disentangling Light Fields for Super-Resolution and Disparity Estimation,
PAMI(45), No. 1, January 2023, pp. 425-443.
IEEE DOI 2212
Estimation, Image processing, Task analysis, Spatial resolution, Image reconstruction, Cameras, Light field image processing, disparity estimation BibRef

Liang, Z.Y.[Zheng-Yu], Wang, Y.Q.[Ying-Qian], Wang, L.G.[Long-Guang], Yang, J.G.[Jun-Gang], Zhou, S.L.[Shi-Lin],
Light Field Image Super-Resolution With Transformers,
SPLetters(29), 2022, pp. 563-567.
IEEE DOI 2202
Transformers, Superresolution, Feature extraction, Spatial resolution, Light fields, Encoding, Convolution, Light field, transformer BibRef

Ko, K., Koh, Y.J., Chang, S., Kim, C.S.,
Light Field Super-Resolution via Adaptive Feature Remixing,
IP(30), 2021, pp. 4114-4128.
IEEE DOI 2104
Spatial resolution, Feature extraction, Image reconstruction, Signal resolution, Superresolution, Convolution, Interpolation, convolutional neural network (CNN) BibRef

Zhang, S.[Shuo], Chang, S.[Song], Lin, Y.F.[You-Fang],
End-to-End Light Field Spatial Super-Resolution Network Using Multiple Epipolar Geometry,
IP(30), 2021, pp. 5956-5968.
IEEE DOI 2107
Spatial resolution, Geometry, Cameras, Learning systems, Image reconstruction, Convolution, Superresolution, Light field, convolutional neural network BibRef

Gao, C.[Chen], Lin, Y.F.[You-Fang], Chang, S.[Song], Zhang, S.[Shuo],
Spatial-Angular Multi-Scale Mechanism for Light Field Spatial Super-Resolution,
NTIRE23(1961-1970)
IEEE DOI 2309
BibRef

Liu, D.Y.[De-Yang], Wu, Q.[Qiang], Huang, Y.[Yan], Huang, X.P.[Xin-Peng], An, P.[Ping],
Learning from EPI-Volume-Stack for Light Field image angular super-resolution,
SP:IC(97), 2021, pp. 116353.
Elsevier DOI 2107
Light field image angular super-resolution, EPI-volume-stack, 3D convolution, Deep learning BibRef

Yao, H.[Hui], Ren, J.[Jieji], Yan, X.C.[Xiang-Chao], Ren, M.J.[Ming-Jun],
Cooperative Light-Field Image Super-Resolution Based on Multi-Modality Embedding and Fusion With Frequency Attention,
SPLetters(29), 2022, pp. 548-552.
IEEE DOI 2202
Feature extraction, Superresolution, Spatial resolution, Image reconstruction, Training, Convolution, Cameras, Light field, frequency attention BibRef

Jin, J.[Jing], Hou, J.H.[Jun-Hui], Chen, J.[Jie], Zeng, H.Q.[Huan-Qiang], Kwong, S.[Sam], Yu, J.Y.[Jing-Yi],
Deep Coarse-to-Fine Dense Light Field Reconstruction With Flexible Sampling and Geometry-Aware Fusion,
PAMI(44), No. 4, April 2022, pp. 1819-1836.
IEEE DOI 2203
Image reconstruction, Geometry, Learning systems, Image resolution, Rendering (computer graphics), Estimation, Cameras, Light field, image-based rendering BibRef

Huang, D.[Detian], Zhu, X.C.[Xian-Cheng], Li, X.R.[Xiao-Rui], Zeng, H.Q.[Huan-Qiang],
CLSR: Cross-Layer Interaction Pyramid Super-Resolution Network,
CirSysVideo(33), No. 11, November 2023, pp. 6273-6287.
IEEE DOI 2311
BibRef

Jin, J.[Jing], Guo, M.[Mantang], Hou, J.H.[Jun-Hui], Liu, H.[Hui], Xiong, H.K.[Hong-Kai],
Light Field Reconstruction via Deep Adaptive Fusion of Hybrid Lenses,
PAMI(45), No. 10, October 2023, pp. 12050-12067.
IEEE DOI 2310
BibRef

Hu, X.J.[Xin-Jue], Zhang, L.[Lin],
Angular-spatial analysis of factors affecting the performance of light field reconstruction,
IET-IPR(16), No. 4, 2022, pp. 1027-1035.
DOI Link 2203
BibRef

Chen, Y.[Yeyao], Jiang, G.Y.[Gang-Yi], Jiang, Z.[Zhidi], Yu, M.[Mei], Ho, Y.S.[Yo-Sung],
Deep Light Field Super-Resolution Using Frequency Domain Analysis and Semantic Prior,
MultMed(24), 2022, pp. 3722-3737.
IEEE DOI 2208
Frequency-domain analysis, Superresolution, Semantics, Estimation, Light fields, Image restoration, Light field super-resolution, convolutional neural network BibRef

Liu, G.S.[Gao-Sheng], Yue, H.J.[Huan-Jing], Wu, J.M.[Jia-Min], Yang, J.Y.[Jing-Yu],
Intra-Inter View Interaction Network for Light Field Image Super-Resolution,
MultMed(25), 2023, pp. 256-266.
IEEE DOI 2301
Spatial resolution, Feature extraction, Correlation, Finite element analysis, Superresolution, Image reconstruction, LF-IINet BibRef

Zhou, S.[Shubo], Hu, L.[Liang], Wang, Y.L.[Yun-Long], Sun, Z.A.[Zhen-An], Zhang, K.[Kunbo], Jiang, X.Q.[Xue-Qin],
AIF-LFNet: All-in-Focus Light Field Super-Resolution Method Considering the Depth-Varying Defocus,
CirSysVideo(33), No. 8, August 2023, pp. 3976-3988.
IEEE DOI 2308
Imaging, Spatial resolution, Lenses, Feature extraction, Degradation, Microoptics, Image reconstruction, Light field imaging, convolutional neural network BibRef

Xiao, Z.[Zeyu], Cheng, Z.[Zhen], Xiong, Z.W.[Zhi-Wei],
Space-Time Super-Resolution for Light Field Videos,
IP(32), 2023, pp. 4785-4799.
IEEE DOI 2309

WWW Link. BibRef

Liu, G.S.[Gao-Sheng], Yue, H.J.[Huan-Jing], Wu, J.[Jiamin], Yang, J.Y.[Jing-Yu],
Efficient Light Field Angular Super-Resolution With Sub-Aperture Feature Learning and Macro-Pixel Upsampling,
MultMed(25), 2023, pp. 6588-6600.
IEEE DOI 2311
BibRef

Sheng, H.[Hao], Wang, S.[Sizhe], Yang, D.[Da], Cong, R.X.[Rui-Xuan], Cui, Z.L.[Zheng-Long], Chen, R.S.[Rong-Shan],
Cross-View Recurrence-Based Self-Supervised Super-Resolution of Light Field,
CirSysVideo(33), No. 12, December 2023, pp. 7252-7266.
IEEE DOI 2312
BibRef

Cong, R.X.[Rui-Xuan], Sheng, H.[Hao], Yang, D.[Da], Cui, Z.L.[Zheng-Long], Chen, R.S.[Rong-Shan],
Exploiting Spatial and Angular Correlations With Deep Efficient Transformers for Light Field Image Super-Resolution,
MultMed(26), 2024, pp. 1421-1435.
IEEE DOI 2402
Transformers, Computational modeling, Superresolution, Spatial resolution, Feature extraction, Light fields, Convolution, multi-scale angular modeling BibRef

Zhang, S.[Shuo], Lin, Y.F.[You-Fang], Sheng, H.[Hao],
Residual Networks for Light Field Image Super-Resolution,
CVPR19(11038-11047).
IEEE DOI 2002
BibRef

Liu, Y.T.[Yu-Tong], Cheng, Z.[Zhen], Xiao, Z.[Zeyu], Xiong, Z.W.[Zhi-Wei],
Light Field Super-Resolution Using Decoupled Selective Matching,
CirSysVideo(34), No. 5, May 2024, pp. 3313-3326.
IEEE DOI Code:
WWW Link. 2405
Superresolution, Feature extraction, Spatial resolution, Data mining, Light fields, Transformers, Task analysis, Light field, selective matching BibRef


Xiao, Z.[Zeyu], Gao, R.[Ruisheng], Liu, Y.T.[Yu-Tong], Zhang, Y.[Yueyi], Xiong, Z.W.[Zhi-Wei],
Toward Real-World Light Field Super-Resolution,
LightField23(3408-3418)
IEEE DOI 2309
BibRef

Jin, K.[Kai], Yang, A.[Angulia], Wei, Z.[Zeqiang], Guo, S.[Sha], Gao, M.Z.[Ming-Zhi], Zhou, X.Z.[Xiu-Zhuang],
DistgEPIT: Enhanced Disparity Learning for Light Field Image Super-Resolution,
NTIRE23(1373-1383)
IEEE DOI 2309
BibRef

Salem, A.[Ahmed], Ibrahem, H.[Hatem], Kang, H.S.[Hyun-Soo],
Learning Epipolar-Spatial Relationship for Light Field Image Super-Resolution,
NTIRE23(1336-1345)
IEEE DOI 2309
BibRef

Xiao, Z.[Zeyu], Liu, Y.T.[Yu-Tong], Gao, R.[Ruisheng], Xiong, Z.W.[Zhi-Wei],
CutMIB: Boosting Light Field Super-Resolution via Multi-View Image Blending,
CVPR23(1672-1682)
IEEE DOI 2309
BibRef

Gendy, G.[Garas], Sabor, N.[Nabil], Hou, J.C.[Jing-Chao], He, G.H.[Guang-Hui],
Real-time Channel Mixing Net for Mobile Image Super-resolution,
AIM22(573-590).
Springer DOI 2304
BibRef

Kar, A.[Aupendu], Nehra, S.[Suresh], Mukhopadhyay, J.[Jayanta], Biswas, P.K.[Prabir Kumar],
Sub-Aperture Feature Adaptation in Single Image Super-Resolution Model for Light Field Imaging,
ICIP22(3451-3455)
IEEE DOI 2211
Photography, Multiplexing, Adaptation models, Superresolution, Cameras, Light fields, Spatial resolution, Light field, super-resolution BibRef

Zhou, L.J.[Lin-Jie], Gao, W.[Wei], Li, G.[Ge],
End-to-End Spatial-Angular Light Field Super-Resolution Using Parallax Structure Preservation Strategy,
ICIP22(3396-3400)
IEEE DOI 2211
Geometry, Visualization, Image synthesis, Superresolution, Estimation, Cameras, Light fields, Super-resolution, light field, image restoration BibRef

Wang, Z.J.[Zi-Jian], Lu, Y.[Yao],
Multi-Granularity Aggregation Transformer for Light Field Image Super-Resolution,
ICIP22(261-265)
IEEE DOI 2211
Limiting, Superresolution, Transformers, Light fields, Spatial resolution, Image reconstruction, Mutual information, Visual Attention BibRef

Schambach, M.[Maximilian], Shi, J.Y.[Jia-Yang], Heizmann, M.[Michael],
Spectral Reconstruction and Disparity from Spatio-Spectrally Coded Light Fields via Multi-Task Deep Learning,
3DV21(186-196)
IEEE DOI 2201
Deep learning, Training, Superresolution, Estimation, Cameras, Multitasking, multispectral light field, computational imaging, multi task deep learning BibRef

Ma, D., Lumsdaine, A., Zhou, W.,
Flexible Spatial and Angular Light Field Super Resolution,
ICIP20(2970-2974)
IEEE DOI 2011
Spatial resolution, Convolution, Kernel, Feature extraction, Training, Neural networks, Light Field Super Resolution, Convolution Neural Network BibRef

Jin, J., Hou, J., Chen, J., Kwong, S.,
Light Field Spatial Super-Resolution via Deep Combinatorial Geometry Embedding and Structural Consistency Regularization,
CVPR20(2257-2266)
IEEE DOI 2008
Spatial resolution, Geometry, Image reconstruction, Correlation, Learning systems BibRef

Farag, S., Velisavljevic, V.,
A Novel Disparity-Assisted Block Matching-Based Approach for Super-Resolution of Light Field Images,
3DTV-CON18(1-4)
IEEE DOI 1812
image enhancement, image matching, image resolution, interpolation, classical image super-resolution, 4D Imaging BibRef

Alain, M., Smolic, A.,
Light Field Super-Resolution via LFBM5D Sparse Coding,
ICIP18(2501-2505)
IEEE DOI 1809
Spatial resolution, Noise reduction, Discrete cosine transforms, Image edge detection, Light Fields, Guided Image Filtering BibRef

Zheng, H.T.[Hai-Tian], Guo, M.H.[Ming-Hao], Wang, H.Q.[Hao-Qian], Liu, Y.B.[Ye-Bin], Fang, L.[Lu],
Combining Exemplar-Based Approach and learning-Based Approach for Light Field Super-Resolution Using a Hybrid Imaging System,
Multiview17(2481-2486)
IEEE DOI 1802
Cameras, Hardware, Robustness, Spatial resolution BibRef

Wang, Y., Hou, G., Sun, Z., Wang, Z., Tan, T.,
A simple and robust super resolution method for light field images,
ICIP16(1459-1463)
IEEE DOI 1610
Cameras BibRef

Ohashi, K.[Kazuki], Takahashi, K.[Keita], Tehrani, M.P.[Mehrdad Panahpour], Fujii, T.[Toshiaki],
Super-resolution image synthesis using the physical pixel arrangement of a light field camera,
ICIP15(2964-2968)
IEEE DOI 1512
light field camera, plenoptic camera, super-resolution image synthesis BibRef

Wu, J.D.[Ju-Dong], Wang, H.Q.[Hao-Qian], Wang, X.Z.[Xing-Zheng], Zhang, Y.B.[Yong-Bing],
A novel light field super-resolution framework based on hybrid imaging system,
VCIP15(1-4)
IEEE DOI 1605
Cameras BibRef

Nakashima, R.[Ryo], Takahashi, K.[Keita], Naemura, T.[Takeshi],
Theoretical Analysis of Multi-view Camera Arrangement and Light-Field Super-Resolution,
PSIVT11(I: 407-420).
Springer DOI 1111
BibRef

Bishop, T.E., Zanetti, S., Favaro, P.,
Light field superresolution,
ICCP09(1-9).
IEEE DOI 1208
BibRef

Lim, J.G.[Jae-Guyn], Ok, H.W.[Hyun-Wook], Park, B.K.[Byung-Kwan], Kang, J.Y.[Joo-Young], Lee, S.D.[Seong-Deok],
Improving the spatail resolution based on 4D light field data,
ICIP09(1173-1176).
IEEE DOI 0911
BibRef

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


Last update:Jun 17, 2024 at 21:38:11