本书主要描述了基于压缩感知的理论，恢复算法，还有详细介绍了应用，特别适合学习压缩感知的人使用

This paper is a survey of the theory and methods of photogrammetric bundle adjustment, aimed at potential implementors in the computer vision community. Bundle adjustment is the problem of refining a visual reconstruction to produce jointly optimal structure and viewing parameter estimates. Topics covered include: the choice of cost function and robustness; numerical optimization including sparse Newton methods, linearly convergent approximations, updating and recursive methods; gauge (datum) invariance; and quality control. The theory is developed for general robust cost functions rather than restricting attention to traditional nonlinear least squares.

一篇关于稀疏子空间聚类算法、理论的概述性论文，可以用于参考

CS294A Lecture notes Sparse autoencoder，Andrew Ng，Stanford University

这是一本关于稀疏表达在信号和图像处理中运用的权威书籍《Sparse and Redundant Representations From Theory to Applications in Signal and Image Processing》，也是第一部关于稀疏表达的书籍。是稀疏表达的大牛Michael Elad编写的。现在在卓越上有的卖，不过要7百多元，太贵，还是搞电子版看实惠点。

MATLAB 图像稀疏表示代码，可以实现对输入图像的稀疏表示

大牛ELAD关于sparse representation的代码

Direct Methods for Sparse Matrices (I. S. Duff, A. M. Erisman, J. K. Reid).pdf

This textbook introduces sparse and redundant representations with a focus on applications in signal and image processing. The theoretical and numerical foundations are tackled before the applications are discussed. Mathematical modeling for signal sources is discussed along with how to use the proper model for tasks such as denoising, restoration, separation, interpolation and extrapolation, compression, sampling, analysis and synthesis, detection, recognition, and more. The presentation is elegant and engaging. Sparse and Redundant Representations is intended for graduate students in applied mathematics and electrical engineering, as well as applied mathematicians, engineers, and researchers who are active in the fields of signal and image processing. * Introduces theoretical and numerical foundations before tackling applications * Discusses how to use the proper model for various situations * Introduces sparse and redundant representations * Focuses on applications in signal and image processing The field of sparse and redundant representation modeling has gone through a major revolution in the past two decades. This started with a series of algorithms for approximating the sparsest solutions of linear systems of equations, later to be followed by surprising theoretical results that guarantee these algorithms’ performance. With these contributions in place, major barriers in making this model practical and applicable were removed, and sparsity and redundancy became central, leading to state-of-the-art results in various disciplines. One of the main beneficiaries of this progress is the field of image processing, where this model has been shown to lead to unprecedented performance in various applications. This book provides a comprehensive view of the topic of sparse and redundant representation modeling, and its use in signal and image processing. It offers a systematic and ordered exposure to the theoretical foundations of this data model, the numerical aspec

囊括了当今压缩感知领域信号(图像)稀疏表示重构的经典算法(BP,MP,OMP,StOMP,IST,PFP...),由于本人上传文件限额的限制，这里重点只给大家上传了算法的matlab版code(Solvers文件夹中)