Rotationally invariant similarity measures for nonlocal image denoising

Authors:
Sven Grewenig;Sebastian Zimmer;Joachim Weickert
Affiliations:
Mathematical Image Analysis Group, Saarland University, Building E1.1, 66123 Saarbrücken, Germany;Katholieke Universiteit Leuven, ESAT-PSI/VISICS, Kasteelpark Arenberg 10, Bus 2441, 3001 Leuven-Heverlee, Belgium;Mathematical Image Analysis Group, Saarland University, Building E1.1, 66123 Saarbrücken, Germany
Venue:
Journal of Visual Communication and Image Representation
Year:
2011

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Abstract

Many natural or texture images contain structures that appear several times in the image. One of the denoising filters that successfully take advantage of such repetitive regions is NL means. Unfortunately, the block matching of NL means cannot handle rotation or mirroring. In this paper, we analyse two natural approaches for a rotationally invariant similarity measure that will be used as an alternative to, respectively a modification of the well-known block matching algorithm in nonlocal means denoising. The first approach is based on moment invariants whereas the second one estimates the rotation angle, rotates the block via interpolation and then uses a standard block matching. In contrast to the standard method, the presented algorithms can find similar regions or patches in an image even if they appear in several rotated or mirrored instances. Hence, one can find more suitable regions for the weighted average and yield improved results.