Bayesian Deep Learning for Image Reconstruction: From structured sparsity to uncertainty estimation

被引：3

作者：

Dong, Weisheng ^{[1
,2
]}

Wu, Jinjian ^{[3
,4
]}

Li, Leida ^{[5
]}

Shi, Guangming ^{[6
,7
]}

Li, Xin ^{[8
,9
]}

机构：

[1] Hong Kong Polytech Univ, Dept Comp, Hong Kong, Peoples R China

[2] Xidian Univ, Sch Artificial Intelligence, Xian 710071, Peoples R China

[3] Nanyang Technol Univ, Singapore, Singapore

[4] Xidian Univ, Sch Artificial Intelligence, Xian 710071, Peoples R China

[5] Nanyang Technol Univ, Sch Elect & Elect Engn, Rapid Rich Object Search Lab, Singapore, Singapore

[6] Xidian Univ, Sch Elect Engn, Xian, Peoples R China

[7] Xidian Univ, Sch Artificial Intelligence, Xian 710071, Peoples R China

[8] Univ Sci & Technol China, Elect Engn & informat Sci, Hefei, Peoples R China

[9] West Virginia Univ, Lane Dept Comp Sci & Elect Engn, Morgantown, WV 26506 USA

来源：

IEEE SIGNAL PROCESSING MAGAZINE | 2023年 / 40卷 / 01期

基金：

美国国家科学基金会; 国家重点研发计划;

关键词：

Deep learning; Uncertainty; Image coding; Computational modeling; Imaging; Signal processing algorithms; Network architecture; REPRESENTATION; RESTORATION; NETWORK;

D O I：

10.1109/MSP.2022.3176421

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Conventional wisdom in model-based computational imaging incorporates physics-based imaging models, noise characteristics, and image priors into a unified Bayesian framework. Rapid advances in deep learning have inspired a new generation of data-driven computational imaging systems with performances even better than those of their model-based counterparts. However, the design of learning-based algorithms for computational imaging often lacks transparency, making it difficult to optimize the entire imaging system in a complete manner.

引用

页码：73 / 84

页数：12

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