Abstract
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.
Highlights
For a long time, CCD has had advantages of high quantum efficiency, high sensitivity, low dark current, good consistency and low noise compared with CMOS image sensors (CISs)
These column stripes are caused by a fixed pattern noise (FPN) characteristic of CIS
FPN consists of two parts: pixel fixed pattern noise (PFPN) and column fixed pattern noise (CPFN)
Summary
CCD has had advantages of high quantum efficiency, high sensitivity, low dark current, good consistency and low noise compared with CMOS image sensors (CISs). These column stripes are caused by a fixed pattern noise (FPN) characteristic of CIS. The generation of FPN is mainly due to the mismatch of CIS pixel structure and readout structure [1,2]. FPN consists of two parts: pixel fixed pattern noise (PFPN) and column fixed pattern noise (CPFN)
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