Restoration for Weakly Blurred and Strongly Noisy

Abstract

In this paper we present an adaptive sharpening algorithm for restoration of an image which has been corrupted by mild blur, and strong noise. Most existing adaptive sharpening algorithms can not handle strong noise well due to the intrinsic contradiction between sharpening and de-noising. To solve this problem we propose an algorithm that is capable of capturing local image structure and sharpness, and adjusting sharpening accordingly so that it effectively combines denoising and sharpening together without either noise magnification or over-sharpening artifacts. It also uses structure information from the luminance channel to remove artifacts in the chrominance channels. Experiments illustrate that compared with other sharpening approaches, our method can produce state of the art results under practical imaging conditions.