Metric performance in similar blocks search and their use in collaborative 3D filtering of grayscale images

摘要

Similar blocks (patches) search plays an important role in image processing. However, there are many factors making this search problematic and leading to errors. Noise in images that arises due to bad acquisition conditions or other sources is one of the main factors. Performance of similar patch search might make worse dramatically if noise level is high and/or if noise is not additive, white and Gaussian. In this paper, we consider the influence of similarity metrics (distances) on search performance. We demonstrate that robustness of similarity metrics is a crucial issue for performance of similarity search. Two models of additive noise are used: AWGN and spatially correlated noise with a wide set of noise standard deviations. To investigate metric performance, five test images are used for artificially inserted group of identical blocks. Metric effectiveness evaluation is carried out for nine different metric (including several unconventional ones) in three domains (one spatial and two spectral). It is shown that conventional Euclidian metric might be not the best choice which depends upon noise properties and data processing domain. After establishing the best metrics, they are exploited within non-local image denoising, namely the BM3D filter. This filter is applied to intensity images of the database TID2008. It is demonstrated that the use of more robust metrics instead of classical ones (Euclidean) in BM3D filter allows improving similar block search and, as a result, provides better results of image denoising for the case of spatially correlated noise.