Abstract
In this paper we propose a novel approach for reconstruction of images with additive Gaussian noise. In order to solve this inverse problem we use backward stochastic differential equations. Model of the image reconstruction is driven by two stochastic processes. One process has values in domain of the image, and second one in codomain. Appropriate construction of these processes leads to smoothing (anisotropic diffusion) and enhancing filters. Our numerical experiments show that the new algorithm gives very good results and compares favourably with classical Perona-Malik method.
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Borkowski, D. (2010). Smoothing, Enhancing Filters in Terms of Backward Stochastic Differential Equations. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds) Computer Vision and Graphics. ICCVG 2010. Lecture Notes in Computer Science, vol 6374. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15910-7_26
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DOI: https://doi.org/10.1007/978-3-642-15910-7_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15909-1
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