Abstract
Natural images consist of texture, structure and smooth regions and this makes the task of filtering challenging mainly when it aims at edge and texture preservation. In this paper, we present a novel adaptive filtering technique based on a partition of the image to ”noisy smooth zones” and ”texture or edge + noise” zones. To this end, an analysis of local features is used to recover a statistical model that associates to each pixel a probability measure corresponding to a membership degree for each class. This probability function is then encoded in a new denoising process based on a MPM (Marginal Posterior Mode) estimation technique. The posterior density is computed through a non parametric density estimation method with variable kernel bandwidth that aims to adapt the denoising process to image structure. In our algorithm the selection of the bandwidth relies on a non linear function of the membership probabilities. Encouraging, experimental results demonstrate the potential of our approach.
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Azzabou, N., Paragios, N., Guichard, F. (2007). Uniform and Textured Regions Separation in Natural Images Towards MPM Adaptive Denoising. In: Sgallari, F., Murli, A., Paragios, N. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2007. Lecture Notes in Computer Science, vol 4485. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72823-8_36
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DOI: https://doi.org/10.1007/978-3-540-72823-8_36
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