18.4.3.2 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., 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


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

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 .


Last update:Apr 11, 2021 at 21:43:28