Low-rank matrix approximations, such as the truncated singular value decomposition and
the rank-revealing QR decomposition, play a central role in data analysis and scientific …

We consider the problem of recovering a complete (ie, square and invertible) matrix A 0,
from Y∈ R n× p with Y= A 0 X 0, provided X 0 is sufficiently sparse. This recovery problem is …

图像的多尺度几何分析:回顾和展望 Page 1 图像的多尺度几何分析:回顾和展望 焦李成,谭山 (西安
电子科技大学雷达信号处理国家重点实验室和智能信息处理研究所,陕西西安710071) 摘要: 多尺度 …

This first chapter formulates the objectives of compressive sensing. It introduces the
standard compressive problem studied throughout the book and reveals its ubiquity in many …

While deep learning methods have achieved state-of-the-art performance in many
challenging inverse problems like image inpainting and super-resolution, they invariably …

Suppose x is an unknown vector in Ropf m (a digital image or signal); we plan to measure n
general linear functionals of x and then reconstruct. If x is known to be compressible by …

Suppose we are given a vector< emphasis>< formula formulatype=" inline">< tex>
f</tex></formula></emphasis> in a class< emphasis>< formula formulatype=" inline">< tex> …

Time-frequency analysis is a modern branch of harmonic analysis. It com prises all those
parts of mathematics and its applications that use the struc ture of translations and …

The limitations of commonly used separable extensions of one-dimensional transforms,
such as the Fourier and wavelet transforms, in capturing the geometry of image edges are …

The goal of the sparse approximation problem is to approximate a target signal using a
linear combination of a few elementary signals drawn from a fixed collection. This paper …