This review article provides a comprehensive survey on state-of-the-art impulse and
Gaussian denoising filters applied to images and summarizes the progress that has been …

The goal of this paper is to present a non-iterative and more importantly an extremely fast
algorithm to reconstruct images from compressively sensed (CS) random measurements. To …

Recent deep network-based compressive sensing (CS) methods have achieved great
success. However, most of them regard different sampling matrices as different independent …

A denoising algorithm seeks to remove noise, errors, or perturbations from a signal.
Extensive research has been devoted to this arena over the last several decades, and as a …

We propose a novel method for compressed sensing recovery using untrained deep
generative models. Our method is based on the recently proposed Deep Image Prior (DIP) …

Image nonlocal self-similarity (NSS) property has been widely exploited via various sparsity
models such as joint sparsity (JS) and group sparse coding (GSC). However, the existing …

Group sparse representation (GSR) has made great strides in image restoration producing
superior performance, realized through employing a powerful mechanism to integrate the …

Traditional algorithms for compressive sensing recovery are computationally expensive and
are ineffective at low measurement rates. In this paper, we propose a data-driven …

This paper presents a novel strategy for high-fidelity image restoration by characterizing
both local smoothness and nonlocal self-similarity of natural images in a unified statistical …

Deep learning has recently been intensively studied in the context of image compressive
sensing (CS) to discover and represent complicated image structures. These approaches …