Compressive sensing allows the reconstruction of original signals from a much smaller number samples as compared to Nyquist sampling rate. The effectiveness compressive motivated researchers for its deployment in variety application areas. use an efficient matrix high-performance recovery algorithms improves performance framework significantly. This paper presents underlying concepts well previ...

Decomposition of digital signals and images into other basis or dictionaries than time space domains is a very common approach in signal image processing analysis. Such decomposition commonly obtained using fixed transforms (e.g., Fourier wavelet) learned from example databases the itself. In this work, we investigate detail new constructing such image-dependent bases inspired by quantum mechan...

The Gaussian process is an increasingly well-known type of stochastic process, which a generalization the probability distribution. It allows us to model complex functions thanks its flexibility, would not be possible with use other tools. processes also have couple features that are used in various branches automation positive results, ranging from industrial image processing. There many ways ...

The diffusion magnetic resonance imaging (MRI) signal arising from biological tissues can be numerically simulated by solving the Bloch–Torrey partial differential equation. Numerical simulations facilitate investigation of relationship between MRI signals and cellular structures. With rapid advance available computing power, community has begun to employ numerical for model formulation validat...

Signal expansions using frames may be considered as generalizations of signal representations based on transforms and filter banks. Frames for sparse signal representations may be designed using an iterative method with two main steps: (1) Frame vector selection and expansion coefficient determination for signals in a training set, – selected to be representative of the signals for which compac...

Learning sparsifying dictionaries from a set of training signals has been shown to have much better performance than pre-designed dictionaries in many signal processing tasks, including image enhancement. To this aim, numerous practical dictionary learning (DL) algorithms have been proposed over the last decade. This paper introduces an accelerated DL algorithm based on iterative proximal metho...