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
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
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.[Youfang],
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
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
Hu, X.[Xinjue],
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
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
Liu, G.[Gaosheng],
Yue, H.[Huanjing],
Wu, J.[Jiamin],
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, 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
Zhang, S.[Shuo],
Lin, Y.[Youfang],
Sheng, H.[Hao],
Residual Networks for Light Field Image Super-Resolution,
CVPR19(11038-11047).
IEEE DOI
2002
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 .