12 July 2023 Glow and hot pixels removal using improved robust principal component analysis
Yihao Zhang, Hongfei Zhang, Ziyi Liu, Jie Zhu, Jiayao Gu, Xingbo Wang, Zeyu Zhu, Yujing Tang, Jian Wang
Author Affiliations +
Abstract

In low-light-level detection, glow and hot pixels in some imaging sensors become visible due to long-exposure time, leading to image quality degradation. To solve the problem of glow and hot pixels in a single image, an improved extraction algorithm based on the idea of robust principal component analysis is proposed to remove them. The image is divided into three terms in our algorithm: a low-rank matrix (image without glow and hot pixels), an extremely sparse matrix (hot pixels), and a sparse and spatially smooth matrix (glow). Specifically, the total variation norm and ℓ1-norm are exploited to describe the property of glow. Moreover, a top-hat filter and a boundary-searching method are introduced into the soft threshold operator to improve accuracy. The superiority of the proposed approach is demonstrated with evaluations on simulated datasets, quantitative metrics, and real data.

© 2023 SPIE and IS&T
Yihao Zhang, Hongfei Zhang, Ziyi Liu, Jie Zhu, Jiayao Gu, Xingbo Wang, Zeyu Zhu, Yujing Tang, and Jian Wang "Glow and hot pixels removal using improved robust principal component analysis," Journal of Electronic Imaging 32(4), 043012 (12 July 2023). https://doi.org/10.1117/1.JEI.32.4.043012
Received: 25 February 2023; Accepted: 21 June 2023; Published: 12 July 2023
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KEYWORDS
Image processing

Matrices

Principal component analysis

Tunable filters

Optical filters

Cameras

Computer simulations

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