Closed-Form Nonlocal Shrinkage for Multiplicative Image Denoising and SAR Despeckling
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 go...
| Authors | Xuran Hu, Mingzhe Zhu, Djordje Stanković et al. |
|---|---|
| Primary category | cs.CV |
| Published | 15 August 2026 |
| Last updated at source | 6 September 2026 |
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