SAR image denoising based on multifractal feature analysis and TV regularisation

Abstract

A new denoising technique is proposed in this study for synthetic aperture radar (SAR) images corrupted by speckle noise. The authors method extract informative features from a noisy speckled image, and then a denoised version of this image is estimated from the informative gradients, which are restricted to the features of this image. The technique of extracting features is designed on the framework of multifractal formalism followed by a reconstruction technique for the informative gradients based on the total variational (TV) regularisation framework. Experimental results demonstrate that the proposed approach is able to retain the finer details of the original image while removing noise. The superiority of the proposed approach is manifested qualitatively and quantitatively on comparing with state-of-the-art denoising techniques.

A SAR Image-Despeckling Method Based on HOSVD Using Tensor Patches

“…Despite being more effective than spatial domain despeckling methods, transform domain filtering demands substantial computational resources, can cause image blurring, and introduce artifacts, which adversely affect subsequent image processing. Regularization-based despeckling is another popular approach to SAR images [14,15], which transforms the despeckling problem into a minimization energy function. Regularization models based on the total variance model can effectively suppress speckle noise and maintain texture.…”