Laser & Optoelectronics Progress, Volume. 61, Issue 16, 1611015(2024)
Deep Learning-Based Light-Field Image Restoration and Enhancement: A Survey (Invited)
[11] Yang J C, Everett M, Buehler C et al. A real-time distributed light field camera[M]. Eurographics workshop on rendering, 77-85(2002).
[13] Ng R, Levoy M, Brédif M et al. Light field photography with a hand-held plenoptic camera[D](2005).
[15] Unger J, Wenger A, Hawkins T et al. Capturing and Rendering with Incident Light Fields[C], 141-149(2003).
[18] Engl H W, Groetsch C W[M]. Inverse and ill-posed problems(2014).
[25] Farrugia R A, Guillemot C. Light field super-resolution using a low-rank prior and deep convolutional neural networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 1162-1175(2020).
[63] Wu G C, Liu Y B, Fang L et al. Revisiting light field rendering with deep anti-aliasing neural network[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44, 5430-5444(2022).
[64] Nair V, Hinton G E. Rectified linear units improve restricted boltzmann machines[C], 807-814(2010).
Get Citation
Copy Citation Text
Zeyu Xiao, Zhiwei Xiong, Lizhi Wang, Hua Huang. Deep Learning-Based Light-Field Image Restoration and Enhancement: A Survey (Invited)[J]. Laser & Optoelectronics Progress, 2024, 61(16): 1611015
Category: Imaging Systems
Received: Jun. 3, 2024
Accepted: Jul. 11, 2024
Published Online: Aug. 12, 2024
The Author Email: Zhiwei Xiong (zwxiong@ustc.edu.cn)
CSTR:32186.14.LOP241404