Single-Patch Low-Rank Prior for Non-pointwise Impulse Noise Removal

Ruixuan Wang, Emanuele Trucco; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2013, pp. 1073-1080

This paper introduces a 'low-rank prior' for small oriented noise-free image patches: considering an oriented patch as a matrix, a low-rank matrix approximation is enough to preserve the texture details in the properly oriented patch. Based on this prior, we propose a single-patch method within a generalized joint low-rank and sparse matrix recovery framework to simultaneously detect and remove non-pointwise random-valued impulse noise (e.g., very small blobs). A weighting matrix is incorporated in the framework to encode an initial estimate of the spatial noise distribution. An accelerated proximal gradient method is adapted to estimate the optimal noise-free image patches. Experiments show the effectiveness of our framework in removing non-pointwise random-valued impulse noise.