We address the problem of smoothing gray-level images without destroying feature carriers. Smoothing is performed to suppress high, spatial-frequency noise in the image, whose relevant features contain high spatial-frequency components. The separation is obtained by using a heuristical image-surface geometry criterion over 5x5 mask. Pixel classification results with bit-fields associated with image processing tasks such as noise suppression, edge and/or some 2D-features extraction. We demonstrate the results on standard benchmark image disturbed by uncorrelated gaussian noise. Peformance of some filters applied to feature-less domains of the image is compared.
CITATION STYLE
Kasinski, A. J. (1997). Smoothing noisy images without destroying predefined feature carriers. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1296, pp. 419–526). Springer Verlag. https://doi.org/10.1007/3-540-63460-6_158
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