Journal of Applied Optics, Volume. 45, Issue 5, 956(2024)
Light field images super-resolution based on sub-pixel and gradient guide
[1] JIA C, SHI F, ZHAO M. Object detection based on light field imaging[C], 239-244(2022).
[2] HAN L, SHI Z, ZHENG S N et al. Light field depth estimation using RNN and CRF[C], 725-729(2022).
[3] SONG Lizheng, LIN Dongyun, PENG Xiafu et al. Patch-match binocular 3D reconstruction based on deep learning[J]. Journal of Applied Optics, 43, 436-443(2022).
[4] XU Y F, ZHAO J K. Deep multi-levels edge-guided network for super-resolution[C], 1-5(2022).
[5] LIU Y H, LI S M, LIU A Q. Two-way guided super-resolution reconstruction network based on gradient prior[C], 1819-1823(2021).
[6] YOON Y, JEON H G, YOO D et al. Light field image super-resolution using convolutional neural network[J]. IEEE Signal Processing Letters, 24, 848-852(2017).
[7] WANG Y Q, WANG L G, YANG J G et al. Spatial-angular interaction for light field image super-resolution[C], 290-308(2020).
[8] 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 Transactions on Image Processing, 28, 2319-2330(2019).
[9] WU G C, ZHAO M D, WANG L Y et al. Light field reconstruction using deep convolutional network on EPI[C], 1638-1646(2017).
[10] AN Ping, CHEN Xin, CHEN Yilei et al. Light field super-resolution based on viewpoint image and EPI feature fusion[J]. Signal Processing, 38, 1818-1830(2022).
[11] ZHANG S, LIN Y, SHENG H. Residual networks for light field image super-resolution[C], 11038-11047(2019).
[12] ZHANG S, CHANG S, LIN Y. End-to-end light field spatial super-resolution network using multiple epipolar geometry[J]. IEEE Transactions on Image Processing, 30, 5956-5968(2021).
[13] 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).
[14] WANG Y Q, YANG J G, WANG L G et al. Light field image super-resolution using deformable convolution[J]. IEEE Transactions on Image Processing, 30, 1057-1071(2021).
[15] WANG S Z, ZHOU T F, LU Y et al. Detail-preserving transformer for light field image super-resolution[J]. AAAI Conference on Artificial Intelligence, 36, 2522-2530(2022).
[18] HUI Z, GAO X B, YANG Y C et al. Lightweight image super-resolution with information multi-distillation network[C], 2024-2032(2019).
[19] RERABEK M, EBRAHIMI T. New light field image dataset[C], 2409457(2016).
[20] HONAUER K, JOHANNSEN O, KONDERMANN D et al. A dataset and evaluation methodology for depth estimation on 4D light fields[C], 19-34(2016).
[21] WANNER S, MEISTER S, GOLDLUECKE B. Datasets and benchmarks for densely sampled 4D light fields[J]. Vision, Modelling and Visualization, 13, 225-226.(2013).
[22] PENDU M L, JIANG X R, GUILLEMOT C. Light field in painting propagation via low rank matrix completion[J]. IEEE Transactions on Image Processing, 27, 1981-1993(2018).
[24] KIM J, LEE J K, LEE K M. Accurate image super-resolution using very deep convolutional networks[C], 1646-1654(2016).
[25] LIM B, SON S, KIM H et al. Enhanced deep residual networks for single image super-resolution[C], 1132-1140(2017).
Get Citation
Copy Citation Text
Wei WEI, Fen CHEN, Huabo ZHANG, Yingguo LUO, Peng ZHANG, Zongju PENG. Light field images super-resolution based on sub-pixel and gradient guide[J]. Journal of Applied Optics, 2024, 45(5): 956
Category:
Received: Jun. 25, 2023
Accepted: --
Published Online: Dec. 20, 2024
The Author Email: Fen CHEN (陈芬)