ICCD low-light-level image denoising based on robust principal component analysis

Yu Wang; Yanyang Liu; Peng Zhao; Xulei Qin; Ye Tao; Ye Li

doi:10.1117/12.2605359

24 November 2021 ICCD low-light-level image denoising based on robust principal component analysis

Yu Wang, Yanyang Liu, Peng Zhao, Xulei Qin, Ye Tao, Ye Li

Proceedings Volume 12065, AOPC 2021: Optical Sensing and Imaging Technology; 120651B (2021) https://doi.org/10.1117/12.2605359
Event: Applied Optics and Photonics China 2021, 2021, Beijing, China

Abstract

In this paper, a method of removing ion-feedback noise based on RPCA and median filter is proposed, a removal mechanism based on iterative strategy of "detection-location-removal" is established to remove the noise step by step, and BM3D algorithm is used to remove the Gaussian noise. The experimental results show that the proposed method can effectively remove the noise, and protect the edges and details of ICCD LLL images as much as possible. In addition, we quantitatively evaluate the denoising performance of the method. Our method obtains better objective measurement values. It has better effectiveness and robustness for ICCD LLL image denoising.

Citation Download Citation

Yu Wang, Yanyang Liu, Peng Zhao, Xulei Qin, Ye Tao, and Ye Li "ICCD low-light-level image denoising based on robust principal component analysis", Proc. SPIE 12065, AOPC 2021: Optical Sensing and Imaging Technology, 120651B (24 November 2021); https://doi.org/10.1117/12.2605359

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KEYWORDS

Denoising

Digital filtering

Signal to noise ratio

Image filtering

Image denoising

Principal component analysis

Image restoration

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