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.,
Hou, J.,
Chen, X.,
Chen, J.,
Chen, Z.,
Chung, Y.Y.,
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, computer vision, convolution, convolutional neural nets,
feature extraction, image reconstruction, image resolution,
convolutional neural networks
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
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
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
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
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
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],
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
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
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
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
Generative Adversarial Network, Neural Netowrks for Super Resolution .