[1] Van Duong V, Huu T N, Yim J et al. Light field image super-resolution network via joint spatial-angular and epipolar information[J]. IEEE Trans Comput Imaging, 9, 350-366(2023).

[2] Wang C C, Zang Y S, Zhou D M et al. Robust multi-focus image fusion using focus property detection and deep image matting[J]. Expert Syst Appl, 237, 121389(2024).

[3] Sakem A, Ibrahem H, Kang H S. Learning epipolar-spatial relationship for light field image super-resolution[C], 1336-1345(2023). https://doi.org/10.1109/CVPRW59228.2023.00140

[4] Cui Z L, Sheng H, Yang D et al. Light field depth estimation for non-Lambertian objects via adaptive cross operator[J]. IEEE Trans Circuits Syst Video Technol, 34, 1199-1211(2024).

[5] Chao W T, Wang X C, Wang Y Q et al. Learning sub-pixel disparity distribution for light field depth estimation[J]. IEEE Trans Comput Imaging, 9, 1126-1138(2023).

[6] Wang Y Q, Wang L G, Wu G C et al. Disentangling light fields for super-resolution and disparity estimation[J]. IEEE Trans Pattern Anal Mach Intell, 45, 425-443(2023).

[7] Ding Y Q, Chen Z, Ji Y et al. Light field-based underwater 3D reconstruction via angular re-sampling[J]. IEEE Trans Comput Imaging, 9, 881-893(2023).

[8] Zhang Z J, Wu Q Y, Deng Y F et al. Structured light field 3-D imaging method based on Hough transform[J]. Laser Technol, 47, 492-499(2023).

[9] Gao W, Fan S L, Li G et al. A thorough benchmark and a new model for light field saliency detection[J]. IEEE Trans Pattern Anal Mach Intell, 45, 8003-8019(2023).

[10] Chen G, Fu H Z, Zhou T et al. Fusion-embedding Siamese network for light field salient object detection[J]. IEEE Trans Multimedia, 26, 984-994(2024).

[11] Chen Y L, Li G Y, An P et al. Light field salient object detection with sparse views via complementary and discriminative interaction network[J]. IEEE Trans Circuits Syst Video Technol, 34, 1070-1085(2024).

[12] Yeung H W F, Hou J H, Chen X M et al. Light field spatial super-resolution using deep efficient spatial-angular separable convolution[J]. IEEE Trans Image Process, 28, 2319-2330(2019).

[13] Wang Y Q, Yang J G, Wang L G et al. Light field image super-resolution using deformable convolution[J]. IEEE Trans Image Process, 30, 1057-1071(2021).

[14] Yoon Y, Jeon H G, Yoo D et al. Learning a deep convolutional network for light-field image super-resolution[C], 24-32(2015). https://doi.org/10.1109/ICCVW.2015.17

[15] Zhang S, Lin Y F, Sheng H. Residual networks for light field image super-resolution[C], 11046-11055(2019). https://doi.org/10.1109/CVPR.2019.01130

[16] Jin J, Hou J H, Chen J et al. Light field spatial super-resolution via deep combinatorial geometry embedding and structural consistency regularization[C], 2260-2269(2020). https://doi.org/10.1109/CVPR42600.2020.00233

[17] Lv T Q, Wu Y C, Zhao X L. Light field image super-resolution network based on angular difference enhancement[J]. Opto-Electron Eng, 50, 220185(2023).

[18] Chan K H, Im S K. Light-field image super-resolution with depth feature by multiple-decouple and fusion module[J]. Electron Lett, 60, e13019(2024).

[19] Liang Z Y, Wang Y Q, Wang L G et al. Angular‐flexible network for light field image super‐resolution[J]. Electron Lett, 57, 921-924(2021).

[20] Wang S Z, Zhou T F, Lu Y et al. Detail-preserving transformer for light field image super-resolution[C], 2522-2530(2022). https://doi.org/10.1609/aaai.v36i3.20153

[21] Wang Y Q, Wang L G, Yang J G et al. Spatial-angular interaction for light field image super-resolution[C], 290-308(2020). https://doi.org/10.1007/978-3-030-58592-1_18

[22] Liu G S, Yue H J, Wu J M et al. Intra-inter view interaction network for light field image super-resolution[J]. IEEE Trans Multimedia, 25, 256-266(2023).

[23] Li Y L, Chen Y Y, Cui Y L et al. LF-UMTI: unsupervised multi-exposure light field image fusion based on multi-scale spatial-angular interaction[J]. Opto-Electron Eng, 51, 240093(2024).

[24] Wu Y C, Wang Y M, Wang A H et al. Light field all-in-focus image fusion based on edge enhanced guided filtering[J]. J Electron Inf Technol, 42, 2293-2301(2020).

[25] Wu Y C, Wang Y M, Liang J et al. Light field all-in-focus image fusion based on spatially-guided angular information[J]. J Vis Commun Image Represent, 72, 102878(2020).

[26] Liu J, Tang J, Wu G S. Residual feature distillation network for lightweight image super-resolution[C], 41-55(2020). https://doi.org/10.1007/978-3-030-67070-2_2

[27] Rerabek M, Ebrahimi T. New light field image dataset[C](2016).

[28] Honauer K, Johannsen O, Kondermann D et al. A dataset and evaluation methodology for depth estimation on 4D light fields[C], 10113, 19-34(2017). https://doi.org/10.1007/978-3-319-54187-7_2

[29] Wanner S, Meister S, Goldluecke B. Datasets and benchmarks for densely sampled 4D light fields[C], 225-226(2013).

[30] Le Pendu M, Jiang X R, Guillemot C. Light field inpainting propagation via low rank matrix completion[J]. IEEE Trans Image Process, 27, 1981-1993(2018).

[31] Vaish V, Adams A. The (new) Stanford light field archive[EB/OL]. http://lightfield.stanford.edu/

[32] Lim B, Son S, Kim H et al. Enhanced deep residual networks for single image super-resolution[C], 136-144(2017). https://doi.org/10.1109/CVPRW.2017.151

[33] Zhang Y L, Li K P, Li K et al. Image super-resolution using very deep residual channel attention networks[C], 286-301(2018). https://doi.org/10.1007/978-3-030-01234-2_18