Speckle is an unavoidable noise-like phenomenon in Synthetic Aperture Radar (SAR) imaging. In order to remove speckle, many despeckling methods have been proposed during the past three decades, including spatial-based methods, transform domain-based methods, and non-local filtering methods. However, SAR images usually contain many different types of regions, including homogeneous and heterogeneous regions. Some filters could despeckle effectively in homogeneous regions but could not preserve structures in heterogeneous regions. Some filters preserve structures well but do not suppress speckle effectively. Following this theory, we design a combination of two state-of-the-art despeckling tools that can overcome their respective shortcomings. In order to select the best filter output for each area in the image, the clustering and Gray Level Co-Occurrence Matrices (GLCM) are used for image classification and weighting, respectively. Clustering and GLCM use the co-registered optical images of SAR images because their structure information is consistent, and the optical images are much cleaner than SAR images. The experimental results on synthetic and real-world SAR images show that our proposed method can provide a better objective performance index under a strong noise level. Subjective visual inspection demonstrates that the proposed method has great potential in preserving structural details and suppressing speckle noise.

Coherent imaging of synthetic aperture radar (SAR) systems generates multiplicative speckle noise, severely impairs SAR images’ interpretability. Although numerous despeckling methods have been proposed over the past three decades, SAR despeckling remains an unsolved problem due to its uniqueness and complexity. To address these issues, we propose a novel SAR image despeckling method based on the framework of weighted nuclear norm minimization (WNNM). First, a Gaussian function is used to approximate the distribution of good similar patches (GSP). Clustering is then used to select the optimal patches for the GSP matrix. WNNM can then estimate the underlying clean component of the GSP matrix. To increase the speed of the WNNM, a modified version of the singular value decomposition (SVD) is introduced. Additionally, we developed a noise variance estimation method that enables blind despeckling with the proposed method. Extensive experimental results demonstrate that the proposed method yields superior objective indices and subjective visual inspection.

Synthetic Aperture Radar (SAR) is a powerful imaging system with all-day and all-weather capabilities, making it suitable for a wide range of applications. However, SAR images often suffer from coherent speckle noise, which degrades image quality and hampers subsequent analysis and interpretation. Recently, methods based on the Fisher-Tippett (FT) distribution and non-local low-rank (NLR) techniques have shown great potential in SAR despeckling. Building upon these methods, this paper proposes a novel SAR image despeckling method named SAR Nuclear Norm minus Frobenius Norm (SAR-NNFN). This method effectively restores clean images using singular value shrinkage and allows for adaptive shrinkage without the need for additional weighting parameters. SAR-NNFN utilizes NNFN to achieve rank relaxation, resulting in a more robust low-rank solution for speckle reduction. The proposed model comprises two components: a data fidelity term that captures the statistical characteristics of SAR images using the FT distribution in the logarithmic domain, and an NNFN regularization term that enhances low-rank approximations. The optimization problem associated with SAR-NNFN is solved using the Alternating Direction Method of Multipliers (ADMM) algorithm. Extensive experiments conducted on both simulated and real SAR images demonstrate that SAR-NNFN can not only adequately suppress speckle noise but also preserve fine textures.

Speckle noise in Synthetic Aperture Radar (SAR) images significantly degrades image quality and complicates image interpretation. Therefore, reducing speckle noise in SAR images is a crucial step. Recently, deep learning-based methods have demonstrated impressive performance in SAR image despeckling. However, due to the unavailability of clean SAR images, supervised despeckling models trained on synthetic noisy images often suffer from poor generalization. To address this challenge, we propose a speckle-driven unsupervised despeckling network named SDUDNet. This network employs a generative adversarial network (GAN) strategy to overcome the lack of paired data by utilizing only unpaired clean and noisy training images. Specifically, the generator in SDUDNet learns the noise distribution in real SAR images and transforms clean images into pseudo-SAR images without any assumptions or priors. Additionally, we introduce a global-local discriminator (GLD) to finely discern subtle features in the generated images. Furthermore, an attention network is integrated to help the discriminator focus on critical details within the images. Experiments conducted on both synthetic and real SAR images demonstrate that SDUDNet achieves state-of-the-art results in terms of visual quality and objective metrics. The source code of our proposed method is available at https://github.com/BFY-official/SDUDNet.

Coherent imaging systems, such as synthetic aperture radar (SAR), often suffer from granular speckle noise due to inherent defects, which can make interpretation challenging. Although numerous despeckling methods have been proposed in the past three decades, SAR image despeckling remains a challenging task. With the extensive use of non-local self-similarity, despeckling methods under the non-local framework have become increasingly mature. However, effectively utilizing patch similarities remains a key problem in SAR image despeckling. This paper proposes a three-dimensional (3D) SAR image despeckling method based on searching for similar patches and applying the high-order singular value decomposition (HOSVD) theory to better utilize the high-dimensional information of similar patches. Specifically, the proposed method extends two-dimensional (2D) to 3D for SAR image despeckling using tensor patches. A new, non-local similar patch-searching measure criterion is used to classify the patches, and similar patches are stacked into 3D tensors. Lastly, the iterative adaptive weighted tensor cyclic approximation is used for SAR image despeckling based on the HOSVD method. Experimental results demonstrate that the proposed method not only effectively reduces speckle noise but also preserves fine details.