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Open AccessArticle

CMOS Fixed Pattern Noise Elimination Based on Sparse Unidirectional Hybrid Total Variation

by 1,2,3, 1,*, 2 and 3
1
Key Laboratory of Adaptive Optics, Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China
2
School of Optoelectronic Information, University of Electronic Science and Technology, Chengdu 611731, China
3
Astronomical Technology Laboratory, Yunnan Observatory, Chinese Academy of Sciences, Kunming 650216, China
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(19), 5567; https://doi.org/10.3390/s20195567
Received: 14 August 2020 / Revised: 22 September 2020 / Accepted: 23 September 2020 / Published: 28 September 2020
(This article belongs to the Special Issue Computational Methods in Imagery (CMI))
With the improvement of semiconductor technology, the performance of CMOS Image Sensor has been greatly improved, reaching the same level as that of CCD in dark current, linearity and readout noise. However, due to the production process, CMOS has higher fix pattern noise than CCD at present. Therefore, the removal of CMOS fixed pattern noise has become the research content of many scholars. For current fixed pattern noise (FPN) removal methods, the most effective one is based on optimization. Therefore, the optimization method has become the focus of many scholars. However, most optimization models only consider the image itself, and rarely consider the structural characteristics of FPN. The proposed sparse unidirectional hybrid total variation (SUTV) algorithm takes into account both the sparse structure of column fix pattern noise (CFPN) and the random properties of pixel fix pattern noise (PFPN), and uses adaptive adjustment strategies for some parameters. From the experimental values of PSNR and SSM as well as the rate of change, the SUTV model meets the design expectations with effective noise reduction and robustness. View Full-Text
Keywords: FPN; sparse; total variation; anisotropy; characteristic FPN; sparse; total variation; anisotropy; characteristic
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MDPI and ACS Style

Zhang, T.; Li, X.; Li, J.; Xu, Z. CMOS Fixed Pattern Noise Elimination Based on Sparse Unidirectional Hybrid Total Variation. Sensors 2020, 20, 5567. https://doi.org/10.3390/s20195567

AMA Style

Zhang T, Li X, Li J, Xu Z. CMOS Fixed Pattern Noise Elimination Based on Sparse Unidirectional Hybrid Total Variation. Sensors. 2020; 20(19):5567. https://doi.org/10.3390/s20195567

Chicago/Turabian Style

Zhang, Tao; Li, Xinyang; Li, Jianfeng; Xu, Zhi. 2020. "CMOS Fixed Pattern Noise Elimination Based on Sparse Unidirectional Hybrid Total Variation" Sensors 20, no. 19: 5567. https://doi.org/10.3390/s20195567

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