Speckle is a granular disturbance, usually modeled as a multiplicative noise, that affects synthetic aperture radar (SAR) images, as well as all coherent images. Over the last three decades, several methods have been proposed for the reduction of speckle, or despeckling, in SAR images. Goal of this paper is making a comprehensive review of despeckling methods since their birth, over thirty years ago, highlighting trends and changing approaches over years. The concept of fully developed speckle is explained. Drawbacks of homomorphic filtering are pointed out. Assets of multiresolution despeckling, as opposite to spatial-domain despeckling, are highlighted. Also advantages of undecimated, or stationary, wavelet transforms over decimated ones are discussed. Bayesian estimators and probability density function (pdf) models in both spatial and multiresolution domains are reviewed. Scale-space varying pdf models, as opposite to scale varying models, are promoted. Promising methods following non-Bayesian approaches, like nonlocal (NL) filtering and total variation (TV) regularization, are reviewed and compared to spatial- and wavelet-domain Bayesian filters. Both established and new trends for assessment of despeckling are presented. A few experiments on simulated data and real COSMO-SkyMed SAR images highlight, on one side the costperformance tradeoff of the different methods, on the other side the effectiveness of solutions purposely designed for SAR heterogeneity and not fully developed speckle. Eventually, upcoming methods based on new concepts of signal processing, like compressive sensing, are foreseen as a new generation of despeckling, after spatial-domain and multiresolution-domain methods.

A Tutorial on Speckle Reduction in Synthetic Aperture Radar Images

A review on medical image denoising algorithms

This letter presents an adaptive filtering approach of synthetic aperture radar (SAR) image times series based on the analysis of the temporal evolution. First, change detection matrices (CDMs) containing information on changed and unchanged pixels are constructed for each spatial position over the time series by implementing coefficient of variation (CV) cross tests. Afterward, the CDM provides for each pixel in each image an adaptive spatiotemporal neighborhood, which is used to derive the filtered value. The proposed approach is illustrated on a time series of 25 ascending TerraSAR-X images acquired from November 6, 2009 to September 25, 2011 over the Chamonix-Mont-Blanc test-site, which includes different kinds of change, such as parking occupation, glacier surface evolution, etc.

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Adaptive Multitemporal SAR Image Filtering Based on the Change Detection Matrix

Regularization method is an effective tool for synthetic aperture radar (SAR) image despeckling. Design of the effective regularization terms describing the image priors plays a vital role in this kind of method. In this article, a new combinational regularization model for speckle reduction (CRM-SR) is proposed, in which a regularization term is elaborately designed to contain both a fractional-order total variation (FrTV) regularization and a nonlocal low rank (NLR) regularization. The new regularization model inherits both the advantages of FrTV and NLR and improves the performance of SAR despeckling and, therefore, better preserves the edges and geometrical features of the images during the despeckling process. An efficient algorithm based on alternating direction optimization is derived to solve the proposed combinational regularization model. Experimental results show that the proposed model can effectively remove SAR image speckle and preserve the geometrical features of images according to both subjective visual assessment of image quality and objective evaluation.

SAR Image Despeckling Based on Combination of Fractional-Order Total Variation and Nonlocal Low Rank Regularization

The method of speckle reduction is widely used in synthetic aperture radar (SAR) imagery over the last three decades The SAR images are inherently speckled in nature Speckle noise is a granular pattern distribution, usually modeled as a multiplicative noise that affects the SAR images, as well as all coherent images Other SAR related problems are also discussed in this paper Therefore, despeckling approaches are needed to improve the quality of SAR images However, there is a trade-off between speckle reduction and the preservation of fine details in the despeckled SAR image The reduction of the speckle noise without losing the fine details of the SAR image is a diffucult task However, many despeckling methods have been discussed to reduce the speckle noise from the SAR images Each method has their own norms, advantages and disadvantages This article contains a review of some major work in the field of SAR image despeckling Often, scientists and scholars have faced the struggle to understand the pattern distribution of the speckle noise in SAR images Hence, a brief details about radar, SAR imaging, speckle noise in SAR images and the prevalent approaches of SAR image despeckling are reviewed here The advantages and disadvantages of SAR image despeckling approaches are also analysed and discussed

A Review on SAR Image and its Despeckling

In this letter, a modified bilateral filter suitable for synthetic aperture radar (SAR) image despeckling, named space-domain filter with alterable window (SFAW), is proposed. SFAW features geometric adaptivity, in both spatial and similarity functions, respectively driven by the following: 1) the local coefficient of variation ( CV) and 2) the joint probability density function model of two pixels having the same reflectivity. Tests on synthetic and real SAR images show that SFAW notably smoothes speckle with unperceivable detail blurring and achieves better performances than other related methods.

Guang-ting Li

Papers

SAR Image Despeckling Using a Space-Domain Filter With Alterable Window

Non-local SAR Image Despeckling Based on Similar Pixels Selected: Non-local SAR Image Despeckling Based on Similar Pixels Selected

The Pixel-similarity Measurement in SAR Image Despeckling: The Pixel-similarity Measurement in SAR Image Despeckling