Fixed pattern noise correction of CMOS image sensor based on dark current

Qiang LI; Long-xu JIN; Guo-ning LI

doi:10.37188/CJLCD.2020-0176

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Fixed pattern noise correction of CMOS image sensor based on dark current

Image Processing | 更新时间：2021-02-07

- Fixed pattern noise correction of CMOS image sensor based on dark current
- Chinese Journal of Liquid Crystals and Displays Vol. 36, Issue 2, Pages: 327-333(2021)
- 作者机构：
  
  中国科学院长春光学精密机械与物理研究所, 吉林长春 130033
- 作者简介：
- 基金信息：
- DOI：10.37188/CJLCD.2020-0176
  CLC： TN492
- Received：07 July 2020，
  
  Revised：16 September 2020，
  
  Accepted：16 September 2020，
  
  Published：2021-02
- 稿件说明：
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Qiang LI, Long-xu JIN, Guo-ning LI. Fixed pattern noise correction of CMOS image sensor based on dark current[J]. Chinese journal of liquid crystals and displays, 2021, 36(2): 327-333.
DOI：

Qiang LI, Long-xu JIN, Guo-ning LI. Fixed pattern noise correction of CMOS image sensor based on dark current[J]. Chinese journal of liquid crystals and displays, 2021, 36(2): 327-333. DOI： 10.37188/CJLCD.2020-0176.

摘要

为了抑制由读出放大电路增益不一致导致的CMOS传感器固定模式噪声（FPN），提高成像质量，本文提出了基于暗电流的固定模式噪声校正方法。该方法相比于传统校正方法具有校正系统简单，算法易于实现的优点。本文对校正前后的图像通过灰度方差进行评估，评估结果表明：由暗电流形成的本底图像，图像灰度方差校正后相比于校正前，降低1~2个数量级；均匀光照条件下的CMOS图像，校正后相比于校正前，图像灰度方差增大2个数量级。该评估结果及相关结论对暗电流本底图像及相关校正方法探讨具有一定的借鉴意义。

Abstract

In order to restrain the effect of CMOS image sensor fixed pattern noise(FPN) caused by the gain mismatch of readout amplifier circuit

to improve the quality of imaging

a method of fixed pattern noise correction based on the dark current is proposed in this paper. Compared with the traditional correction methods

this method has simple correction system

and the algorithm is easy to be implemented. The images before and after correction were evaluated via the grayscale variance

and the evaluation results show that in the background image formed by dark current

the grayscale variance of after corrected image reduced by 4 orders of magnitude compared with that before correction. As for the CMOS image under uniform illumination condition

the grayscale variance of after corrected image increased by 2 orders of magnitude compared with that before correction. The evaluation results and related conclusions are useful for the future study of dark current background images and correlation correction methods.

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Keywords

references

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