HoMeNL: A Homogeneity Measure-Based Nonlocal Filtering Framework for Detail-Enhanced (Pol)(In)SAR Image Denoising

DOI码：10.1016/j.isprsjprs.2023.01.026

发表刊物：ISPRS Journal of Photogrammetry and Remote Sensing

关键字：(Polarimetric) (interferometric) synthetic aperture radar ((Pol)(In)SAR); Nonlocal means (NLM); Homogeneous pixel selection; Heterogeneous pixels removal; Homogeneity measure

摘要：As an inherent problem in coherent imaging systems, the existence of speckle noise results in SAR images with strong signal-dependent variance and seriously hinders the related properties estimation and the image interpretation. Among many filtering methods, nonlocal means (NLM) have been proven to be effective in reducing noise while preserving details. However, traditional NLM filters still face two core problems: 1) it is difficult for homogeneous pixels selection to construct a patch adaptive to local structure for preventing the omission phenomenon; 2) most central pixel value estimators are still at the stage of suppressing the blurring effect rather than eliminating the wrongly selected heterogeneous pixels. To overcome these two problems in the (Pol)(In)SAR image denoising, this paper proposes a homogeneity measure-based nonlocal (HoMeNL) filtering framework based on the following three innovations: 1) to sufficiently select homogeneous pixels in the patch-wise matching processing, the shape-adaptive (SA) patch can be selected from multiple preset patches with the one-to-many matching strategy; 2) as a general extension of the Lee estimator in (Pol)(In)SAR image denoising, the homogeneity measure (HoMe)-based estimator can achieve an optimal bias-variance tradeoff for the central pixel value; 3) the highlight of the proposed method is that the iterative re-weighted (IRW) estimation combines the residuals statistics and the homogeneity measure to adaptively locate and remove the wrongly selected heterogeneous pixels. Simulated and real experimental results show that the proposed filtering framework owns a superior performance than most state-of-art filters in three aspects of noise reduction, detail enhancement, and coherence magnitude estimation.

论文类型：期刊论文

通讯作者：Wang Changcheng

学科门类：工学

文献类型：J

卷号：197

页面范围：212-227

是否译文：否

发表时间：2023-02-12

收录刊物：SCI

发布期刊链接：https://www.sciencedirect.com/science/article/pii/S0924271623000321