Abstract
Image denoising methods depend on inner parameters that control filter strength, so the problem of the filter parameters choice arises. Parameter optimization can be done in the ridge areas, when we can analyze their appearance on the difference between original noisy and filtered image (so-called method noise image). If this difference is irregular, then the filtering strength can be increased. If regular components appear on method noise, then the filtering strength is too large. We use mutual information closely connected with conditional entropy for the analysis and consider images corrupted with Gaussian-like noise with small correlation radius. Ridge detection approach based on Hessian matrix eigenvalues analysis is used for estimation of sizes and directions of image characteristic details. Retinal images containing many ridges of different scales and directions from DRIVE and general images from TID2008 databases with added controlled Gaussian noise were used for testing with NLM and LJNLM-LR methods.
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The work was supported by Russian Science Foundation grant 17-11-01279.
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Mamaev, N., Yurin, D., Krylov, A. (2017). Image Ridge Denoising Using No-Reference Metric. In: Blanc-Talon, J., Penne, R., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2017. Lecture Notes in Computer Science(), vol 10617. Springer, Cham. https://doi.org/10.1007/978-3-319-70353-4_50
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