Doctoral School of Applied Informatics and Applied Mathematics, Óbuda University, Bécsi út 96/B, 1034 Budapest, Hungary.
Institute for Computer Science and Control (SZTAKI), Eötvös Loránd Research Network (ELKH), Kende u. 13-17, 1111 Budapest, Hungary.
Sensors (Basel). 2022 Dec 12;22(24):9733. doi: 10.3390/s22249733.
Flat-field correction (FFC) is commonly used in image signal processing (ISP) to improve the uniformity of image sensor pixels. Image sensor nonuniformity and lens system characteristics have been known to be temperature-dependent. Some machine vision applications, such as visual odometry and single-pixel airborne object tracking, are extremely sensitive to pixel-to-pixel sensitivity variations. Numerous cameras, especially in the fields of infrared imaging and staring cameras, use multiple calibration images to correct for nonuniformities. This paper characterizes the temperature and analog gain dependence of the dark signal nonuniformity (DSNU) and photoresponse nonuniformity (PRNU) of two contemporary global shutter CMOS image sensors for machine vision applications. An optimized hardware architecture is proposed to compensate for nonuniformities, with optional parametric lens shading correction (LSC). Three different performance configurations are outlined for different application areas, costs, and power requirements. For most commercial applications, the correction of LSC suffices. For both DSNU and PRNU, compensation with one or multiple calibration images, captured at different gain and temperature settings are considered. For more demanding applications, the effectiveness, external memory bandwidth, power consumption, implementation, and calibration complexity, as well as the camera manufacturability of different nonuniformity correction approaches were compared.
平面场校正（FFC）常用于图像信号处理（ISP）中，以提高图像传感器像素的均匀性。已经知道图像传感器的不均匀性和镜头系统的特性与温度有关。一些机器视觉应用，如视觉里程计和单像素空中目标跟踪，对像素到像素的灵敏度变化非常敏感。许多相机，特别是在红外成像和凝视相机领域，使用多个校准图像来校正不均匀性。本文描述了两种用于机器视觉应用的现代全局快门 CMOS 图像传感器的暗信号不均匀性（DSNU）和光电响应不均匀性（PRNU）的温度和模拟增益依赖性。提出了一种优化的硬件架构来补偿不均匀性，并具有可选的参数镜头阴影校正（LSC）。概述了三种不同的性能配置，适用于不同的应用领域、成本和功率要求。对于大多数商业应用，LSC 的校正就足够了。对于 DSNU 和 PRNU，可以考虑使用在不同增益和温度设置下捕获的一个或多个校准图像进行补偿。对于更苛刻的应用，比较了不同的非均匀性校正方法的有效性、外部内存带宽、功耗、实现、校准复杂性以及相机的制造可行性。
