Robust Adaptive Denoising of color images with mixed Gaussian and impulsive noise

Abstract

Denoising remains one of the most crucial research areas within image processing given its effect on later analysis. During the different steps of image acquisition, transmission, and storage, noise considerably deteriorates image quality. Very poor image quality may prevent a vision system in some limiting situations from performing properly. In natural images, Gaussian and impulsive noise is probably the most frequent kind of noise and will be tackled in this paper. Various solutions to this problem are given in the contemporary literature, but all these techniques can be further improved for even better results, hence bringing better outcomes in further stages of image processing.

In this paper, we will present a Robust Adaptive Denoising technique (RAD) that makes use of elements of the Non-Local Means mechanism in combination with a new measure of the similarity of image patches, taking into account the impulsiveness component of image noise. The proposed methodology introduces an adaptive selection technique that relieves potential users from the complex process of parameter tuning to get the best results. We have also analyzed problems connected with the size of the patch and processing block, and also with some local weighting aspects.

Extensive experimentation demonstrates that the proposed approach outperforms the performance of existing filters and yields results superior to those obtained by recent neural network-based techniques. The proposed solution can be utilized without the cumbersome network training process, is independent of any a priori knowledge of the mixed noise level and image characteristics.