Studying the influence of search rule and context shape in filtering impulse noise images with Markov chains

The paper presents an improved context-based denoising method for gray scale images affected by impulse noise. The proposed algorithm is using Markov chains to replace the detected noise with the intensity having the highest number of occurrences in similar contexts. The context of a noisy pixel consists in its neighbor pixels and is searched in a larger but limited surrounding area. We have analyzed different search methods and different context shapes. The experimental results obtained on the test images have shown that the most efficient model applies the search in form of   “*”  of contexts in form of   “+”. Besides the better denoising performance obtained on all the noise levels, the computational time has been also significantly improved with respect to our previous context-based filter which applied full search of full context. We have also compared this improved Markov filter with other denoising techniques existing in the literature, most of them being significantly outperformed.