Sparse Reconstruction with Compressed Sensing: Difference between revisions

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Author: Ngoc Ly (SysEn 5800 Fall 2021)
Author: Ngoc Ly (SysEn 5800 Fall 2021)
==== Compressed Sensing (CS)====
Compessed


==  Introduction ==
==  Introduction ==
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Random Gaussian and Bernoulli satisfies RIP
Random Gaussian and Bernoulli satisfies RIP


Let <math>\Phi \in \mathbb{R}^{M \times N}</math> satisfy RIP, Let <math>[N]</math> be an index set For <math>s</math> is a restriction on <math>\mathbf{x}</math> denoted by <math>x_{|s}</math> <math>x \in \mathbb{R}^N</math> to <math>s</math> k-sparse <math>\mathbf{x}</math> s.t. RIP is satisfied the s = | Γ | i.e. s [ N ] and Φ | s ⊆ Φ where the columns of Φ | s is indexed by i S
Let <math>\Phi \in \mathbb{R}^{M \times N}</math> satisfy RIP, Let <math>[N]</math> be an index set For <math>s</math> is a restriction on <math>\mathbf{x}</math> denoted by <math>x_{|s}</math> <math>x \in \mathbb{R}^N</math> to <math>s</math> k-sparse <math>\mathbf{x}</math> s.t. RIP is satisfied the <math>s = |\Gamma|</math> i.e. <math>s \subseteq [N]</math>and <math>\Phi_{|s} \subseteq \Phi</math> where the columns of <math>\Phi_{|s}</math> is indexed by <math>i \in S</math>



Revision as of 05:31, 28 November 2021

Author: Ngoc Ly (SysEn 5800 Fall 2021)

Compressed Sensing (CS)

Compessed

Introduction

often not really sparse but approximately sparse

for is a Random Gaussian or Bernoulli matrix

are the observed y samples

noise vector k e k 2 ≤ η

s.t.

How can we reconstruct x from

The goal is to reconstruct given and

Sensing matrix must satisfy RIP i.e. Random Gaussian or Bernoulli matrixies satisfies (cite)

let be an index set enumerates the columns of and is an under determined systems with infinite solutions. Why norm does not work

What is compression is synonymous with to the sparsity.

The problem formulation is to recover sparse data


The support of is we say is sparse when

We are interested in the smallest , i.e.


Before we get into RIP lets talk about RIC

Restricted Isometry Constant (RIC) is the smallest that satisfies the RIP condition introduced by Candes, Tao

Random Gaussian and Bernoulli satisfies RIP

Let satisfy RIP, Let be an index set For is a restriction on denoted by to k-sparse s.t. RIP is satisfied the i.e. and where the columns of is indexed by

x i , i f i ∈ S

( x | S ) i =

0 otherwise

RIP defined as

( 1 − δ s )k x k 22 ≤ k Φx k 22 ≤ ( 1 + δ s )k x k 22

3 Lemmas Page 267 Blumensath Davies IHT for CS

Lemma 1(Blumensath, Davis 2009 Iterative hard thresholding for compressed

sensing), For all index sets Γ and all Φ for which RIP holds with s = | Γ | that is

s = supp ( x )

1k Φ Γ T k 2 ≤

q

1 + δ | Γ | k y k 2

( 1 − δ | Γ | )k x Γ k 22 ≤ k Φ Γ T Φ Γ x Γ k 22 ≤ ( 1 + δ | Γ | )k x Γ k 22

and

k( I − Φ Γ T Φ Γ )k 2 ≤ δ | Γ | k x Γ k 2

SupposeΓ ∩ Λ = ∅

k Φ Γ T Φ Λ ) x Λ k 2 ≤ δ s k x Λ k 2

Lemma 2 (Needell Tropp, Prop 3.5 in CoSaMP: Iterative signal recovery

from incomplete and inaccurate √ samples)

If Φ satisfies RIP k Φx s k 2 ≤ 1 + δ s k x s k 2 , ∀ x s : k x s k 0 ≤ s, Then ∀ x

k Φx k 2 ≤

p

1 + δ s k x k 2 +

p

1 + δ s

k x k 1

sqrts

Lemma 3 (Needell Tropp, Prop 3.5 in CoSaMP: Iterative signal recovery

from incomplete and inaccurate samples)

Let x s be the best s-term approximation to x. Let x r = x − x s Let

y = Φx + e = Φx s + Φx r + e = Φx s + ẽ

If Φ satisfies RIP for sparsity s, then the norm of error ẽ is bounded by

k ẽ k 2 ≤

p

1 + δ s k x − x s k 2 +

p

1 + δ s

k x − x s k 1

+ k e k 2

s

∀ x

Theory

Gel’fend n-width

Errors E ( S, Φ, D )

Definition Mutual Coherence

LetA ∈ R M × N , themutualcoherenceµ A isde f inedby :

µ A =

|h a i , a j i|

k a i kk a j k

i 6 = j

We want a small µ A because it will be close ot the normal matrix, which

will satisfies RIP

Algorithm IHT

  • Initialize
  • output
  • While halting criterion false do

end while return:

Numerical Example

Iterative Hard Thresholding IHT