The knowledge of the noise level within an image is a valuable information for many image processing applications. Estimating the noise level function (NLF) requires the identification of homogeneous regions, upon which the noise parameters are computed. Sutour et al. have proposed a method to estimate this NLF based on the search for homogeneous regions of square shape. We generalize this method to the search for homogeneous regions with arbitrary shape thanks to the tree of shapes representation of the image under study, thus allowing a more robust and precise estimation of the noise level function.
CITATION STYLE
Esteban, B., Tochon, G., & Géraud, T. (2019). Estimating the Noise Level Function with the Tree of Shapes and Non-parametric Statistics. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11679 LNCS, pp. 377–388). Springer Verlag. https://doi.org/10.1007/978-3-030-29891-3_33
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