I want to create this thread to collect, archive knowledge of large-scale compressive sensing. Hopefully give a good overview to other academics about how to do compressed sensing on high-dimensional, high-resolution data such as images. Recently I worked with Hidden Markov Tree related models; therefore, Duarte's work on "Model-based Compressive Sensing" was brought to my attention. However, like many other methods, the size of input matrix are limited due to the need of creating and storing "sensing matrices" (randomly generated for incoherence with your input signals). Therefore, I can only process up to the size 64x64; it is simply not enough for me. I try to push this boundary and find "noiselet", which could help sense sparse structures of image data. I do some experiments of combining Hidden Markov Tree and Noiselet but there are no positive results so far. That is why I wish to know more about "noiselet" or other methods which can generate good sensing matrices without creating and storing enormous sensing matrices. I appreciate any hints, directions, comments, etc and thanks in advance. Some references:
Model-based Compressive Sensing, RG Baraniuk, V Cevher, MF Duarte, C Hedge,
Using Correlated Subset Structure for Compressive Sensing Recovery - A Divekar, D Needell
Found a nice website about "Fast and Efficient Compressive Sampling using Structurally Random Matrices".
https://sites.google.com/site/thongdojhu/srmintro
The method claims following points:
1. A faster and more structured sampling operator for 2D signals such as natural images.
2. Sampling a natural image efficiently without a need of vectorizing it first .
After reading a bit, I realize my problems are actually named "compressive sampling". How do we design a "good" sensing matrix ?
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