The Secrets of Non-Blind Poisson Deconvolution

被引:2
作者
Gnanasambandam, Abhiram [1 ]
Sanghvi, Yash [2 ]
Chan, Stanley H. [2 ]
机构
[1] Samsung Res Amer, Plano, TX 75023 USA
[2] Purdue Univ, Sch Elect & Comp Engn, W Lafayette, IN 47907 USA
基金
美国国家科学基金会;
关键词
Photon-limited; deconvolution; inverse problems; deblurring; shot noise; IMAGE; ALGORITHM; RECONSTRUCTION; RESTORATION; SIGNAL;
D O I
10.1109/TCI.2024.3369414
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
Non-blind image deconvolution has been studied for several decades but most of the existing work focuses on blur instead of noise. In photon-limited conditions, however, the excessive amount of shot noise makes traditional deconvolution algorithms fail. In searching for reasons why these methods fail, we present a systematic analysis of the Poisson non-blind deconvolution algorithms reported in the literature, covering both classical and deep learning methods. We compile a list of five "secrets" highlighting the do's and don'ts when designing algorithms. Based on this analysis, we build a proof-of-concept method by combining the five secrets. We find that the new method performs on par with some of the latest methods while outperforming some older ones.
引用
收藏
页码:343 / 356
页数:14
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