Abstract
We consider here a Bayesian framework and the respective global algorithm for adaptive image denoising which preserves essential local peculiarities in basically smooth changing of intensity of reconstructed image. The algorithm is based on the special nonstationary gamma-normal statistical model and can handle both Gaussian noise, which is an ubiquitous model in the context of statistical image restoration, and Poissonian noise, which is the most common model for low-intensity imaging used in biomedical imaging. The algorithm being proposed is simple in tuning and has linear computation complexity with respect to the number of image elements so as to be able to process large data sets in a minimal time.
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This research is funded by RFBR, grant #13-07-00529.
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Gracheva, I., Kopylov, A., Krasotkina, O. (2015). Fast Global Image Denoising Algorithm on the Basis of Nonstationary Gamma-Normal Statistical Model. In: Khachay, M., Konstantinova, N., Panchenko, A., Ignatov, D., Labunets, V. (eds) Analysis of Images, Social Networks and Texts. AIST 2015. Communications in Computer and Information Science, vol 542. Springer, Cham. https://doi.org/10.1007/978-3-319-26123-2_7
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