Adaptive Detection and Correction of Fixed Pattern Noise in sCMOS Cameras

In the field of scientific research, there are high requirements for image quality. In recent years, the emergence of scientific CMOS (sCMOS) cameras has provided a favorable tool for this demand, but when applied in special circumstances, there is inevitably appearing fixed pattern noises (FPN), damaging image details. This paper presents a new method for detecting FPN and correcting the detected results adaptively in images. The detection algorithm is divided into dark-scene detection and illuminated- scene detection, dark-scene detection makes use of the simulation of FPN detection, the detection accuracy is up to 99.13%. For the illuminated-scene detection requirements, an adaptive threshold algorithm is proposed. Based on the FPN detection results, performing a 3x3 window median grayscale substitution algorithm to correct them one by one. The experimental results show that the algorithm can detect the position coordinate information of FPN accurately, remove the influence of FPN effectively, and can be widely applied to sCMOS cameras with high requirements for image quality.