Geometry-Calibrated Closed-Form Shrinkage for SAR Despeckling

[2608.15028] Closed-Form Nonlocal Shrinkage for Multiplicative Image Denoising and SAR Despeckling Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Vision and Pattern Recognition arXiv:2608.15028 (cs) [Submitted on 15 Aug 2026 ( v1 ), last revised 6 Sep 2026 (this version, v2)] Title: Closed-Form Nonlocal Shrinkage for Multiplicative Image Denoising and SAR Despeckling Authors: Xuran Hu , Mingzhe Zhu , Djordje Stanković , Yujie Zhu , Zhenpeng Feng , Yifang Ban , Ljubiša Stanković View a PDF of the paper titled Closed-Form Nonlocal Shrinkage for Multiplicative Image Denoising and SAR Despeckling, by Xuran Hu and 6 other authors View PDF HTML (experimental) Abstract: Multiplicative noise poses a challenge in coherent and signal-dependent imaging owing to its intensity-dependent variance and frequently non-Gaussian distribution. We propose a deterministic nonlocal estimator that combines a logarithmic Yeo--Johnson transformation, patch grouping, an adaptive singular basis, and sparse shrinkage. The orthonormal group dictionary makes the weighted Lasso separable and yields an exact coefficient-wise soft-threshold solution. This solution replaces the iterative inner solver and expresses patch reliability and atom importance through a single threshold field. Since the dictionary is estimated from the noisy group, we introduce a random-matrix correction governed by the group aspect ratio $\gamma=p^2/K$. The correction links patch size, group size, and shrinkage strength. Experiments cover gamma-corrupted images from three standard benchmarks and real synthetic aperture radar (SAR) imagery from five sensors. The method gives the best result in 18 of 24 PSNR/SSIM comparisons with twelve published methods and the lowest mean ratio-image deviation across six real SAR configurations. These results support geometry-calibrated nonlocal modeling for structure-preserving image restoration, with SAR despeckling serving as a demanding application. Code is available \href{ this https URL }{here}. Comments: 30 pages, 10 figures Subjects: Computer Vision and Pattern Recognition (cs.CV) ACM classes: I.4.4 Cite as: arXiv:2608.15028 [cs.CV] (or arXiv:2608.15028v2 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2608.15028 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Xuran Hu [ view email ] [v1] Sat, 15 Aug 2026 04:28:27 UTC (33,420 KB) [v2] Sun, 6 Sep 2026 14:59:47 UTC (24,217 KB) Full-text links: Access Paper: View a PDF of the paper titled Closed-Form Nonlocal Shrinkage for Multiplicative Image Denoising and SAR Despeckling, by Xuran Hu and 6 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV < prev | next > new | recent | 2026-08 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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