摘要贪婪算法以其重建速度快, 重建方法实现简便的特点在压缩感知(Compressed sensing,
CS) 理论中获得了广泛的应用. 本文首先介绍压缩感知的基本理论; 然后, 着重介绍现有几种重要 …

Approximate message passing-based compressive sensing reconstruction has received
increasing attention, the performance of which depends heavily on the ability of the …

Penalized least squares regression is often used for signal denoising and inverse problems,
and is commonly interpreted in a Bayesian framework as a Maximum a posteriori (MAP) …

We propose a modified sequential quadratic programming method for solving the sparse
signal recovery problem. We start by going through the well-known smoothed-ℓ 0 technique …

State-space models (SSMs) are a powerful statistical tool for modelling time-varying systems
via a latent state. In these models, the latent state is never directly observed. Instead, a …

Taking advantage of the structures inherent in many sparse decompositions constitutes a
promising research axis. In this paper, we address this problem from a Bayesian point of …

In numerous applications, such as DNA microarrays, face recognition, and spectral
unmixing, we need to acquire a non-negative K-sparse signal x from an underdetermined …

We consider the problem of robust compressed sensing where the objective is to recover a
high-dimensional sparse signal from compressed measurements partially corrupted by …

This paper addresses the sparse representation (SR) problem within a general Bayesian
framework. We show that the Lagrangian formulation of the standard SR problem, ie, x∗ …

Consider the problem of simultaneous estimation and support recovery of the coefficient
vector in a linear data model with additive Gaussian noise. We study the problem of …