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.
Sensors (Basel). 2020 Sep 28;20(19):5567. doi: 10.3390/s20195567.
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.
随着半导体技术的提高，CMOS 图像传感器的性能得到了极大的提高，在暗电流、线性度和读出噪声方面已经达到了与 CCD 相同的水平。然而，由于生产工艺的原因，CMOS 目前的固定模式噪声（FPN）比 CCD 高。因此，去除 CMOS 固定模式噪声已成为许多学者的研究内容。对于当前的固定模式噪声（FPN）去除方法，最有效的方法是基于优化的方法。因此，优化方法成为了许多学者的研究重点。然而，大多数优化模型仅考虑图像本身，很少考虑 FPN 的结构特征。所提出的稀疏单向混合全变分（SUTV）算法同时考虑了列固定模式噪声（CFPN）的稀疏结构和像素固定模式噪声（PFPN）的随机特性，并对某些参数使用了自适应调整策略。从 PSNR 和 SSM 的实验值以及变化率来看，SUTV 模型满足设计预期，具有有效的降噪和鲁棒性。
