CFA Video Denoising and Demosaicking Chain via Spatio-Temporal Patch-Based Filtering

Abstract

Demosaicking and denoising are key steps in the camera imaging chain for both images and videos. The reconstruction errors during these stages will have undesirable effects on the final result if not handled properly. Demosaicking provokes the spatial and color correlation of noise, which is afterwards enhanced by the processing pipeline. This structured noise generally degrades the image quality and, for dark scenes with low signal to noise ratio, prevents the correct interpretation of the image. When trying to mitigate such structured noise on already processed data, denoising methods attenuate details and texture. We present a video processing chain, consisting of a novel strategy for the removal of noise at the camera sensor and a novel video demosaicking algorithm. In both cases, a spatio-temporal patch-based filter with motion compensation is introduced. The experimental results, including real examples, illustrate the performance of the proposed chain, avoiding the creation of interpolation artefacts and colored spots.