Description: 以L1-minimization为核心的算法，近几年飞速进展，Compressive Sensing (Compressive Sampling) 已然成为数学领域和信号处理最前沿最热门的方向。最近一年多这种新形式的算法快速蔓延到模式识别界应用，论文质量高、算法效果好、而且算法一般都非常简单-To L1-minimization as the core of the algorithm, the rapid progress in recent years, Compressive Sensing (Compressive Sampling) has become the field of mathematics and signal processing the forefront of the most popular direction. Recently, more than a year this new form of pattern recognition algorithm for the rapid spread to industry applications, high-quality paper, the algorithm better, but are generally very simple algorithm Platform: |
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Author:yuhua |
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Description: Compressive sampling-Conventional wisdom and common practice in acquisition and reconstruction of
images from frequency data follow the basic principle of the Nyquist density sampling theory.
This principle states that to reconstruct an image, the number of Fourier samples we need to
acquire must match the desired resolution of the image, i.e. the number of pixels in the image.
This paper surveys an emerging theory which goes by the name of “compressive sampling” or
“compressed sensing,” and which says that this conventional wisdom is inaccurate. Perhaps
surprisingly, it is possible to reconstruct images or signals of scientific interest accurately and
sometimes even exactly from a number of samples which is far smaller than the desired resolution
of the image/signal, e.g. the number of pixels in the image Platform: |
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Author:yjsdqq |
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Description: An Introduction To Compressive Sampling-Conventional approaches to sampling signals or images follow Shannon’s celebrated theorem: the sampling rate must be at least twice the maximum frequency present in the signal (the so-called Nyquist rate). In fact, this
principle underlies nearly all signal acquisition protocols used in consumer
audio and visual electronics, medical imaging devices, radio receivers, and
so on. (For some signals, such as images that are not naturally bandlimited, the sampling
rate is dictated not by the Shannon theorem but by the desired temporal or spatial
resolution. However, it is common in such systems to use an antialiasing low-pass filter
to bandlimit the signal before sampling, and so the Shannon theorem plays an implicit
role.) In the field of data conversion, for example, standard analog-to-digital converter
(ADC) technology implements the usual quantized Shannon representation: the signal is
uniformly sampled at or above the Nyquist rate Platform: |
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Author:yjsdqq |
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Description: L1-MAGIC is a collection of MATLAB routines for solving the convex optimization programs central to compressive sampling. The algorithms are based on standard interior-point methods, and are suitable for large-scale problems Platform: |
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Author:thao |
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Description: 关于压缩感知重构算法， 压缩感知（Compressive Sensing, or Compressed Sampling，简称CS），是近几年流行起来的一个介于数学和信息科学的新方向，由Candes、Terres Tao等人提出，挑战传统的采样编码技术，即Nyquist采样定理。-Reconstruction algorithm on compressed sensing, compressive sensing (Compressive Sensing, or Compressed Sampling, referred to as CS), is popular in recent years, a range of mathematics and information science a new direction, by Candes, Terres Tao et al, challenge the traditional sample coding, that Nyquist sampling theorem. Platform: |
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Author:zhongyi |
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Description: 一篇关于压缩感知的经典文章，压缩感知（Compressed sensing，简称CS，也称为Compressive sampling)理论异于近代奈奎斯特采样定理，它指出：利用随机观测矩阵可以把一个稀疏或可压缩的高维信号投影到低维空间上，然后再利用这些少量的投影通过解一个优化问题就可以以高概率重构原始稀疏信号，并且证明了这样的随机投影包含了原始稀疏信号的足够信息。-A classic article on compressed sensing, compressive sensing (Compressed sensing, referred to as CS, also known as Compressive sampling) different from the modern theory of the Nyquist sampling theorem, which states: the use of a random measurement matrix can be sparse or compressible high-dimensional signal projected to low dimensional space, and then use a small amount of projection by solving an optimization problem to be sparse with high probability to reconstruct the original signal, and prove that random projection of the original sparse signal contains enough information. Platform: |
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Author:刘奎 |
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Description: 在这个简短的论述中,我们提供一些基于这一新理论的关键性数学见解,并解释了一些压缩采样和其他领域,如统计学、信息论、编码理论以及理论性的计算机科学之间的交互。-In this short survey, we provide some of the key mathematical insights underlying this new
theory, and explain some of the interactions between compressive sampling and other fields such
as statistics, information theory, coding theory, and theoretical computer science. Platform: |
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Author:tianjingyu |
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Description: 简要分析了压缩感知中的几个重要定理，有一定的借鉴作用。-This paper talks about some important theories in compressive sampling. Platform: |
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Author:haosense |
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Description: Compressive Sampling是最近讨论非常热烈的数据处理方法。用一个与变换基不相关的观测矩阵将变换所得到的高维信号投影到一个低维空间上，然后通过优化求解从这些少量的投影中以高概率重构出原信号。-Compressive Sampling is a very lively discussion of recent data processing method. A base with the transformation matrix will not change the relevant observation obtained a high-dimensional signal projected onto low-dimensional space, and then solved by optimizing the projection from these small reconstructed with high probability in the original signal. Platform: |
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Author:奥利弗 |
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