T2* and quantitative susceptibility mapping (QSM) of magnetic resonance imaging (MRI) images provide different type inner structure information of scanned organs. If they can be properly fused into one set, the details of the scaned organ can be revealed more clearly. In this paper, a 3D MRI image fusion method based on 3D compactly supported shearlet transform (3D-CSST) and 3D dual tree compac...

One open challenge in face recognition (FR) is the single training sample per subject. This paper addresses this problem through a novel approach called Shearlet Network (SN) which takes advantage of the sparse representation (SR) properties of shearlets in biometric applications, specifically, for face coding and recognition. Shearlets are derived from wavelets with composite dilations, a meth...

The analysis and detection of edges and interface boundaries is a fundamental problem in applied mathematics and image processing. In the study of the wave equation, for example, one is interested in the evolution of moving fronts; in image processing and computer vision, the detection and analysis of edges is an essential task for applications such as shape recognition, image enhancement and c...

Microscopic images are becoming important and need to be studied know the details how-to quantitatively evaluate decellularization. Most of existing research focuses on deep learning-based techniques that lack simplification for A new computational method segmentation microscopy based shearlet neural network (SNN) has been introduced. The proposal is link concept shearlets transform networks in...

Fourier Integral Operators appear naturally in a variety of problems related to hyperbolic partial differential equations. While wavelets and other traditional time-frequency methods have been successfully employed for representing many classes of singular integral operators, these methods are not equally effective in dealing with Fourier Integral Operators. In this paper, we show that the shea...

This paper proposes an image interpolation algorithm exploiting sparse representation for natural images. It involves three main steps: (a) obtaining an initial estimate of the high resolution image using linear methods like FIR filtering, (b) promoting sparsity in a selected dictionary through iterative thresholding, and (c) extracting high frequency information from the approximation to refin...