CMOS Fixed Pattern Noise Removal Based on Low Rank Sparse Variational Method
Source abstract
Abstract
Fixed pattern noise (FPN) has always been an important factor affecting the imaging quality of CMOS image sensor (CIS). However, the current scene-based FPN removal methods mostly focus on the image itself, and seldom consider the structure information of the FPN, resulting in various undesirable noise removal effects. This paper presents a scene-based FPN correction method: the low rank sparse variational method (LRSUTV). It combines not only the continuity of the image itself, but also the structural and statistical characteristics of the stripes. At the same time, the low frequency information of the image is combined to achieve adaptive adjustment of some parameters, which simplifies the process of parameter adjustment, to a certain extent. With the help of adaptive parameter adjustment strategy, LRSUTV shows good performance under different intensity of stripe noise, and has high robustness.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Image Processing Techniques and Applications · Image and Signal Denoising Methods · Advanced Image Processing Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Key Laboratory of Adaptive Optics, Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China
School of Optoelectronic Information, University of Electronic Science and Technology, Chengdu 611731, China
Astronomical Technology Laboratory, Yunnan Observatory, Chinese Academy of Sciences, Kunming 650216, China
Funding
National Natural Science Foundation of China
References (43)
Show source-provided references
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