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., Liu, F., Zhang, K., Hou, G., Sun, Z., Tan, T.,
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


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

Liu, Y., Qi, N., Cheng, Z., Liu, D., Ling, Q., Xiong, Z.,
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:Oct 19, 2020 at 15:02:28