Sparsity-based defect pixel compensation for arbitrary camera raw images

In high quality imaging even tiny distortions as small as a single pixel are visible and can not be accepted. Although the production quality of CMOS image sensors is very high, for reasonable yields we still need to accept some defect pixels and clusters of defects in large image sensors. In this paper we will compare compensation algorithms for raw image sensor data. We propose a new approach based on the sparsity assumption that outperforms existing defect compensation algorithms. Furthermore, our proposed interpolation algorithm is universal and not at all adapted to Bayer pattern images. It can directly be applied to any regular color filter pattern or gray scale image. Our examples show, that image sensors with large clusters of defects can still be used for the generation of high quality images.