[1] Xu X Y, Deng H P, Xiang S et al. Light field image super-resolution based on feature interaction fusion and attention mechanism[J]. Laser & Optoelectronics Progress, 60, 1410017(2023).

[2] Ge P, You Y T. Super-resolution reconstruction of light field images via sparse representation[J]. Laser & Optoelectronics Progress, 59, 0210001(2022).

[3] Lü T Q, Wu Y C, Zhao X L. Light field image super-resolution network based on angular difference enhancement[J]. Opto-Electronic Engineering, 50, 0220185(2023).

[4] Xu J, Yuan S N. Image super-resolution reconstruction algorithm based on enhanced multi-scale residual network[J]. Laser & Optoelectronics Progress, 60, 0411002(2023).

[5] Cheng Z, Xiong Z W, Chen C et al. Light field super-resolution: a benchmark[C], 1804-1813(2020).

[6] Lim J, Ok H, Park B et al. Improving the spatail resolution based on 4D light field data[C], 1173-1176(2010).

[7] Nava F P, Luke J P. Simultaneous estimation of super-resolved depth and all-in-focus images from a plenoptic camera[C](2009).

[8] Wanner S, Goldluecke B. Spatial and angular variational super-resolution of 4D light fields[C], 608-621(2012).

[9] Wanner S, Goldluecke B. Variational light field analysis for disparity estimation and super-resolution[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36, 606-619(2014).

[10] Rossi M, Frossard P. Graph-based light field super-resolution[C](2017).

[11] Yoon Y, Jeon H G, Yoo D et al. Learning a deep convolutional network for light-field image super-resolution[C], 57-65(2016).

[12] Zhang S, Lin Y F, Sheng H. Residual networks for light field image super-resolution[C], 11038-11047(2020).

[13] Liang Z Y, Wang Y Q, Wang L G et al. Light field image super-resolution with transformers[J]. IEEE Signal Processing Letters, 29, 563-567(2022).

[14] Wang Y Q, Wang L G, Wu G C et al. Disentangling light fields for super-resolution and disparity estimation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45, 425-443(2023).

[15] Luo Z X, Huang Y, Li S et al. Learning the degradation distribution for blind image super-resolution[EB/OL]. https://arxiv.org/abs/2203.04962

[16] Lempitsky V, Vedaldi A, Ulyanov D. Deep image prior[C], 9446-9454(2018).

[17] Luo Z W, Huang H B, Yu L et al. Deep constrained least squares for blind image super-resolution[C], 17621-17631(2022).

[18] Peng Y F, Zhang M T, Zhang P J et al. Single-image super-resolution reconstruction aggregating residual attention network[J]. Laser & Optoelectronics Progress, 60, 1010017(2023).

[19] Wu L B, Gu Y H, Wu W H et al. Remote sensing rotating object detection based on multi-scale feature extraction[J]. Laser & Optoelectronics Progress, 60, 1228010(2023).

[20] Sitzmann V, Martel J N P, Bergman A W et al. Implicit neural representations with periodic activation functions[EB/OL]. https://arxiv.org/abs/2006.09661

[21] Zhang H M, Dong B. A review on deep learning in medical image reconstruction[J]. Journal of the Operations Research Society of China, 8, 311-340(2020).

[22] Liu P F, Xiao L. A fast algorithm for image restoration based on Hessian nuclear norm regularization[J]. Acta Electronica Sinica, 43, 2001-2008(2015).

[23] Lefkimmiatis S, Ward J P, Unser M. Hessian schatten-norm regularization for linear inverse problems[J]. IEEE Transactions on Image Processing, 22, 1873-1888(2013).

[24] Rudin L I, Osher S, Fatemi E. Nonlinear total variation based noise removal algorithms[J]. Physica D: Nonlinear Phenomena, 60, 259-268(1992).

[25] Mahendran A, Vedaldi A. Understanding deep image representations by inverting them[C], 5188-5196(2015).

[26] Ronneberger O, Fischer P, Brox T, Navab N, Hornegger J, Wells W M et al. U-Net: convolutional networks for biomedical image segmentation[M]. Medical image computing and computer-assisted intervention-MICCAI 2015. Lecture notes in computer science, 9351, 234-241(2015).

[27] Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift[EB/OL]. https://arxiv.org/abs/1502.03167

[28] Glorot X, Bordes A, Bengio Y. Deep sparse rectifier neural networks[C], 315-323(2011).

[29] Zhao B N, Zhang X S, Li Z et al. A multi-scale strategy for deep semantic segmentation with convolutional neural networks[J]. Neurocomputing, 365, 273-284(2019).

[30] Yu Z J, Wang G D, Zhang X Y. Image deblurring based on enhanced multiscale feature network[J]. Laser & Optoelectronics Progress, 59, 2215007(2022).

[31] Honauer K, Johannsen O, Kondermann D, Lai S H, Lepetit V, Nishino K et al. A dataset and evaluation methodology for depth estimation on 4D light fields[M]. Computer vision-ACCV 2016. Lecture notes in computer science, 10113, 19-34(2017).

[32] Wanner S, Meister S, Goldlücke B. Datasets and benchmarks for densely sampled 4D light fields[EB/OL]. http://diglib.eg.org/handle/10.2312/PE.VMV.VMV13.225-226

[33] Raj A S, Lowney M, Shah R et al. Stanford lytro light field archive[EB/OL]. https://scholar.google.com/scholar_lookup?title=Stanford+Lytro+Light+Field+Archive&author=Raj,+A.S.&author=Lowney,+M.&author=Shah,+R.&author=Wetzstein,+G.&publication_year=2016.

[34] Rerabek M, Ebrahimi T. New light field image dataset[EB/OL]. https://infoscience.epfl.ch/record/218363

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

[36] Duchon C E. Lanczos filtering in one and two dimensions[J]. Journal of Applied Meteorology, 18, 1016-1022(1979).

[37] Kingma D P, Ba J. Adam: a method for stochastic optimization[EB/OL]. https://arxiv.org/abs/1412.6980

[38] Li Z Y, Arora S. An exponential learning rate schedule for deep learning[EB/OL]. https://arxiv.org/abs/1910.07454

[39] Huynh-Thu Q, Ghanbari M. Scope of validity of PSNR in image/video quality assessment[J]. Electronics Letters, 44, 800-801(2008).

[40] Wang Z, Bovik A C, Sheikh H R et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 13, 600-612(2004).

[41] Zhang R, Isola P, Efros A A et al. The unreasonable effectiveness of deep features as a perceptual metric[C], 586-595(2018).

[42] Shi W Z, Caballero J, Huszár F et al. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network[C], 1874-1883(2016).

[43] Gao H Y, Yuan H, Wang Z Y et al. Pixel transposed convolutional networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 1218-1227(2020).