Journal Article

PUBDB-2024-07527

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Adaptive total variational regularization of Gaussian denoisers for multiplicative noise removal

Shi, K. (Corresponding author)Extern*DESY*

2024
Elsevier Science Amsterdam [u.a.]

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Please use a persistent id in citations: doi:10.1016/j.camwa.2024.06.017  doi:10.3204/PUBDB-2024-07527

Abstract: 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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Contributing Institute(s):

1. Computational Imaging (FS-CI)

Research Program(s):

1. 623 - Data Management and Analysis (POF4-623) (POF4-623)

Experiment(s):

1. No specific instrument

Appears in the scientific report 2024

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Database coverage:
; ; Clarivate Analytics Master Journal List ; Current Contents - Engineering, Computing and Technology ; Ebsco Academic Search ; Essential Science Indicators ; IF < 5 ; JCR ; Nationallizenz ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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