摘要稀疏表示模型常利用训练样本学习过完备字典, 旨在获得信号的冗余稀疏表示. 设计简单,
高效, 通用性强的字典学习算法是目前的主要研究方向之一, 也是信息领域的研究热点 …

Tensors, or multidimensional arrays, are data structures that can naturally represent visual
data of multiple dimensions. Inherently able to efficiently capture structured, latent semantic …

In recent years, a large amount of multi-disciplinary research has been conducted on sparse
models and their applications. In statistics and machine learning, the sparsity principle is …

Learning filters to produce sparse image representations in terms of overcomplete
dictionaries has emerged as a powerful way to create image features for many different …

This book revolves around the question of designing a matrix D∈ Rm× n called dictionary,
such that to obtain good sparse representations y≈ Dx for a class of signals y∈ Rm given …

Sparse representation has shown to be a very powerful model for real world signals, and
has enabled the development of applications with notable performance. Combined with the …

Sparse representation (SR) has been widely used in image fusion in recent years. However,
source image, segmented into vectors, reduces correlation and structural information of …

Many modern tools in machine learning and signal processing, such as sparse dictionary
learning, principal component analysis, non-negative matrix factorization, K-means …

Dynamic textures (DTs) are video sequences with stationary properties, which exhibit
repetitive patterns over space and time. This paper aims at investigating the sparse coding …

Traditional synthesis/analysis sparse representation models signals in a one dimensional
(1D) way, in which a multidimensional (MD) signal is converted into a 1D vector. 1D …