Showing posts with label Compressed Sensing. Show all posts
Showing posts with label Compressed Sensing. Show all posts
Thursday, September 27, 2012
Sampling Rate is Not Only about Pixels: How to compare the sampling rate between your camera and "single-pixel" camera
At the beginning, I'd like to make clear two terms: "Nyquist frequency" and "Nyquist rate". Some of you may take ...
How to understand "Nyquist sample v.s. CS"?
In this blog, I will discuss from a communication system point of view about the Nyquist sample and CS theory. A communication system usual...
Wednesday, September 26, 2012
Sparse Representation, Compressed Sensing, Single Measurement Vector, Multiple Measurement Vector: connections and differences
Purpose of this blog entry Sparse representation (SR), compressed sensing (CS), single measurement vector (SMV), multiple measuremen...
Friday, August 10, 2012
Basic Understanding of Compressed Sensing
Purpose of this blog entry During my research on CS, one problem is that I have read several papers related on CS, but different paper h...
Friday, March 2, 2012
Summary of Compressed Video Sensing
[1] proposed to use cs on stream video by sample several frame together or independently.... But it didn't consider the interframe redun...
Review of the size of Measurement Matrix in Compressed Sensing
* This article is only a review for my personal use. There may have some mistakes. Please do not trust in this article. * If you noticed an...
Saturday, January 28, 2012
Summary of Block Compressed Sensing on Image
Last article, I briefly introduce existing cs method on image. This article aims to summary how block cs worked on image. Before I go on...
Wednesday, January 25, 2012
Compressed Sensing(压缩感知)用于图像的综述
CS最初由[1][2][3]提出。[1, 2]没有看,[3]中理论推导比较多。我建议读[4]作为入门。 最初的CS理论只提出了noiseless的情况下,可以accurately recover compressible signal,注意是需要compressible sig...
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