Confidence-based Denoising Relying on a Transformation-invariant, Robust Patch Similarity - Exploring Ways to Improve Patch Synchronous Summation

Patch-based denoising techniques have proved to be very efficient. Indeed, they account for the correlations that exist among the patches of natural images, even when degraded by noise. In this context, we propose a denoising method which tries to minimize over-smoothing of textured areas (an effect observed with NLmeans), to avoid staircase effects in monotonically varying areas (an effect observed with BM3D), and to limit spurious patterns in areas with virtually no variations. The first step of the proposed method is to perform patch denoising by averaging similar patches of the noisy image (the equivalent in the space of patches to synchronous summation for temporal signals). From there, our contribution is twofold. (a) We proposed to combine the resulting overlapping denoised patches accounting for an assessed patch denoising confidence. (b) Since a crucial aspect is the definition of a similarity between two patches, we defined a patch similarity invariant to some transformations and robust to noise thanks to a polynomial patch approximation, instead of a classical weighted L2-similarity. The experimental results show an arguably better visual quality of images denoised using the proposed method compared to NL-means and BM3D. In terms of PSNR, the results are significantly above NL-means and comparable to BM3D.