Nonlocal Similarity Image Filtering
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A de-texturing and spatially constrained K-means approach for image segmentation
Pattern Recognition Letters
Edge preserving image denoising with a closed form solution
Pattern Recognition
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This paper investigates the problem of image denoising when the image is corrupted by additive white Gaussian noise. We herein propose a spatial adaptive denoising method which is based on an averaging process performed on a set of Markov Chain Monte-Carlo simulations of region partition maps constrained to be spatially piecewise uniform (i.e., constant in the grey level value sense) for each estimated constant-value regions. For the estimation of these region partition maps, we have adopted the unsupervised Markovian framework in which parameters are automatically estimated in the least square sense. This sequential averaging allows to obtain, under our image model, an approximation of the image to be recovered in the minimal mean square sense error. The experiments reported in this paper demonstrate that the discussed method performs competitively and sometimes better than the best existing state-of-the-art wavelet-based denoising methods in benchmark tests