Removal of speckle noise from Synthetic Aperture Radar (SAR) images is an important step before performing any image processing operations on these images. This paper presents a novel Stationary Wavelet Transform (SWT) based technique for the purpose of removing the speckle noise from the SAR returns. Maximum a posteriori probability (MAP) condition which uses a prior knowledge is used to estim...

SAR (Synthetic Aperture Radar) imaging plays a central role in Remote Sensing due to, among other important features, its ability to provide high-resolution, day-and-night and almost weather-independent images. SAR images are affected from a granular contamination, speckle, that can be described by a multiplicative model. Many despeckling techniques have been proposed in the literature, as well...

The major issue associated with ultrasound imaging is that it may be corrupted by speckle noise during image acquicition. Speckle pattern is a form of multiplicative noise which blurs the ultrasound images. It reduces the contrast and resolution of ultrasound images which results in poor interpretation of image features. Hence speckle reduction is often used as a preprocessing step for successf...

Despeckling optical coherence tomograms from the human retina is a fundamental step to a better diagnosis or as a preprocessing stage for retinal layer segmentation. Both of these applications are particularly important in monitoring the progression of retinal disorders. In this study we propose a new formulation for a well-known nonlinear complex diffusion filter. A regularization factor is no...

Stroke is one of the most important causes of death in the world and the leading cause of serious, long-term disability. There is an urgent need for better techniques to diagnose patients at risk of stroke based on the measurements of the intima media thickness (IMT) and the segmentation of the atherosclerotic carotid plaque. The objective of this work was to carry out a comparative evaluation ...

The convex optimization-based descriptive experiment design regularization (DEDR) method is aggregated with the neural network (NN)-adapted variational analysis (VA) approach for adaptive high-resolution sensing into a unified DEDR -VA-NN framework that puts in a single optimization frame high-resolution radar/SAR image formation in uncertain operational scenarios, adaptive despeckling and dyna...

Speckle fluctuations seriously limit the interpretability of synthetic aperture radar (SAR) images. reduction has thus been subject numerous works spanning at least four decades. Techniques based on deep neural networks have recently achieved a new level performance in terms SAR image restoration quality. Beyond design suitable network architectures or selection adequate loss functions, constru...