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001     619279
005     20250723173050.0
024 7 _ |a 10.1016/j.camwa.2024.06.017
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100 1 _ |a Shi, Kehan
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245 _ _ |a Adaptive total variational regularization of Gaussian denoisers for multiplicative noise removal
260 _ _ |a Amsterdam [u.a.]
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520 _ _ |a In this paper, a convex variational model based on the adaptive total variation (TV) regularization is proposedfor image restoration under multiplicative noise. The adaptive weight allows for greater smoothing in the brightregion for the suppression of speckles. The model includes a nonconvex data fidelity term and also a quadraticpenalty term that enforces the restored image to be close to a reference image deduced from a Gaussian denoiser.It can be viewed as the adaptive TV regularization of the Regularization by Denoising (RED) approach formultiplicative noise removal. We prove that the model admits a unique minimizer in a suitable function spaceand provide a fast numerical algorithm based on the alternating direction method with multipliers (ADMM) forit. Different Gaussian denoisers, including the patch-based algorithm BM3D and the learning-based algorithmDnCNN, are considered for the model in numerical experiments. It is shown that our model efficiently removesmultiplicative noise without introducing artifacts.
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|t Computers & Mathematics with Applications
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773 _ _ |a 10.1016/j.camwa.2024.06.017
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856 4 _ |u https://bib-pubdb1.desy.de/record/619279/files/1-s2.0-S0898122124002852-main%20%281%29.pdf
856 4 _ |y Published on 2024-08-15. Available in OpenAccess from 2025-08-15.
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856 4 _ |y Published on 2024-08-15. Available in OpenAccess from 2025-08-15.
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