Synthetic Aperture Radar (SAR) images suffer of multiplicative speckle noise, which damages the radiometric resolution of SAR images and makes the data interpretation difficult. Bayesian shrinkage in a transformed domain is a well-known method based on finding threshold value to suppress the speckle noise. This paper present a new approach to obtain the optimum threshold values for Bayesian shr...

Convolutional Neural Networks (CNNs) are a type of deep neural network which have performed well at image and audio classification. One approach to understanding the success of CNNs is Mallat’s scattering transform, which formalizes the observation that the filters learned by a CNN have wavelet-like structure. The resulting transform generates a representation that is approximately translation ...

Steganalysis is a technique to detect the hidden embedded information in the provided data. This study proposes a novel steganalytic algorithm which distinguishes between the normal and the stego image. III level contourlet is exploited in this study. Contourlet is known for its ability to capture the intrinsic geometrical structure of an image. Here, the lowest frequency component of each leve...

The main cause of skin cancer is the ultraviolet radiation sun. It spreads quickly to other body parts. Thus, early diagnosis required decrease mortality rate due cancer. In this study, an automatic system for Skin Lesion Classification (SLC) using Non-Subsampled Shearlet Transform (NSST) based energy features and Support Vector Machine (SVM) classifier proposed. At first, NSST used decompositi...

Driven by an overwhelming amount of applications numerical approximation of partial differential equations was established as one of the core areas in applied mathematics. During the last decades a trend for the solution of PDEs emerged, that focuses on employing systems from applied harmonic analysis for the adaptive solution of these equations. Most notably wavelet systems have been used and ...

Shearlets emerged in recent years among the most successful frameworks for the efficient representation of multidimensional data. Indeed, after it was recognized that traditional multiscale methods are not very efficient at capturing edges and other anisotropic features which frequently dominate multidimensional phenomena, several methods were introduced to overcome their limitations. The shear...