An Improved Approach For Mixed Noise Removal In Color Images

Denoising is a fundamental problem in image processing. Mixed noise removal from natural images is a challenging task since the noise distribution usually does not have a parametric model and has a heavy tail. Two types of commonly encountered noise are additive white Gaussian noise (AWGN) and impulse noise (IN). Many of the existing mixed noise removal methods are detection based methods. They first detect the locations of IN pixels and then remove the mixed noise. However, they tend to introduce artifacts. In this paper, we propose a simple method using weighted encoding coupled with alpha-trimmed mean filter to remove the mixed noise distribution effectively. The performance of our approach is experimentally verified on a variety of images and noise levels. The results presented here demonstrate that our proposed method is exceeding the current state of the art methods, both visually and quantitatively. Keywords— Alpha-trimmed mean, adaptive, fuzzy filter, mixed noise removal, nonlocal, sparse representation, PCA dictionary,