Abstract
The task of image decomposition is to split an image into piecewise smooth structural and difference texture-noise components. It is used in many tasks of video information processing and analyzing. The problem of decomposition is to provide independent smoothing in each of the structural regions of the image and to preserve the signal structure. Most of the known methods of decomposition and smoothing are based on analysis of measurable parameters of the local image area, for example, the distribution of signal values. These data does not reflect image area characteristics well enough. Obvious criterion for spatial limiting of the analyzed area is the belonging of the target and surrounding points to the same image spatial area. A sufficient criterion for the connectivity of the points in a region is the absence of contour edges between them. The article proposes an approach to the construction of a decomposition algorithm based on a preliminary delineation of image areas by detecting contours between them and subsequent contour-limited smoothing inside each of the areas. The concept of similarity of points in the image is introduced, on the basis of which the smoothing algorithm is built. Experimental comparisons of the proposed algorithm with other well-known smoothing algorithms are carried out.
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Chochia, P.A. (2021). Image Decomposition Based on Region-Constrained Smoothing. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12665. Springer, Cham. https://doi.org/10.1007/978-3-030-68821-9_10
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DOI: https://doi.org/10.1007/978-3-030-68821-9_10
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