# Noiselet and Structure Compressed Sensing Research & Trend?

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

Model-based Compressive Sensing, RG Baraniuk, V Cevher, MF Duarte, C Hedge,

Using Correlated Subset Structure for Compressive Sensing Recovery - A Divekar, D Needell

## All Answers (2)

Anh Cat Le Ngo· University of Nottingham, Malaysia Campushttps://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 .

Anh Cat Le Ngo· University of Nottingham, Malaysia CampusCan you help by adding an answer?