用于SAR去噪的几何校准闭式收缩
Closed-Form Nonlocal Shrinkage for Multiplicative Image Denoising and SAR Despeckling
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- School of Electronic Engineering, Xidian University（西安电子科技大学电子工程学院）
- Kunshan Innovation Institute of Xidian University（西安电子科技大学昆山创新研究院）
- University of Montenegro（黑山大学）
- KTH Royal Institute of Technology（瑞典皇家理工学院）
- Macquarie University（麦考瑞大学）
机构由 AI 辅助整理，请以论文原文为准。
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中文总结 AI 辅助
本文提出一种几何校准闭式收缩的非局部稀疏估计器，无需训练，在SAR去噪的PSNR/SSIM及比值图像偏差指标上优于多数现有方法。
中文摘要 AI 辅助
合成孔径雷达（SAR）去噪是一个逆恢复问题，必须在不擦除散射结构的前提下抑制乘性非高斯噪声。本文重新研究了一种非局部稀疏估计器，该估计器采用对数-Yeo-Johnson变换，将相似图像块堆叠成组，在其自身左奇异基上对每个组进行编码，并对所得系数进行收缩。该构造将三个通常视为可调的量确定为固定值：其一，组字典是正交的，因此加权Lasso允许精确的逐元素软阈值解，无需迭代内部求解器，且两个表观权重矩阵是单个阈值场的分子和分母，而非独立模块；其二，由于字典由含噪组本身估计得到，其保留的子空间会按组纵横比γ=p²/K吸收斑点，随机矩阵论证将对应的正则化常数转化为几何校准校正，并将图像块尺寸、组尺寸和收缩尺度合并为一个经分析确定的自由度；其三，奇异投影在所有测试视数下均使系数噪声近似高斯分布，从而确定了精确斑点似然不再具有信息量的节点。所得估计器是确定性的、无需训练，且对每张图像和传感器均应用一组经分析确定的设置。在三个合成基准上与12种已发表方法的24次PSNR/SSIM对比中，该估计器在18次对比中排名第一，且在来自5个传感器的6种真实SAR配置上，达到了比值图像与理论斑点模型的最低平均偏差。代码可在此处获取。
英文摘要
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 $γ=p^2/K$. 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{https://github.com/Teriri1999/Geometry-Calibrated-Closed-Form-Shrinkage-for-SAR-Despeckling}{here}.
