Noisy image block matching based on dissimilarity measure in discrete cosine transform domain

In this paper, the problem of image block similarity measuring in noisy environment is considered. In different practical applications often is necessary to find groups of similar image blocks within an ample search area. In such situation, the full search algorithm is very slow; apart, its accuracy is low due to the presence of noise. New algorithms for similar image block matching in noisy environment are presented. The algorithms are based on the dissimilarity measure calculated as the distance between image patches in the discrete cosine transform domain. The proposed algorithms perform the hierarchical search for the similar image blocks and hereby have a reduced complexity in comparison to the full search algorithm. Adjusting the radius of the distance calculation for spectral coefficient matching, the characteristics of the block matching algorithm can easily be adjusted to obtain a better accuracy of the matched block group. A higher accuracy is obtained using the local adaptation of the radius for the distance calculation outperforming the existing algorithms used to find groups of similar blocks in different applications, such as image noise filtering and image clustering. The performance of the different block matching algorithms were evaluated on the base of the proposed accuracy measure that uses as a reference the list of patches obtained with the full search algorithm in the absence of noise.