Abstract
In this paper, we examine an important problem in the context of image processing which is image denoising. Although conventional Gaussian distributions have been widely used, they fail to fit the shape of heavy-tailed data produced by the presence of noise. In this paper, we propose an unsupervised algorithm based on finite mixtures of bounded generalized Gaussian distributions (BGGMD) to achieve smooth denoising results. The proposed framework has the flexibility to fit different shapes of observed data and bounded support data in the case of noisy images. Experimental results demonstrate that the proposed method has superior performance than some conventional approaches.
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Channoufi, I., Bourouis, S., Bouguila, N., Hamrouni, K. (2018). A Flexible Statistical Model for Image Denoising. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds) Image Analysis and Recognition. ICIAR 2018. Lecture Notes in Computer Science(), vol 10882. Springer, Cham. https://doi.org/10.1007/978-3-319-93000-8_4
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DOI: https://doi.org/10.1007/978-3-319-93000-8_4
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