Sparse Reconstruction with Compressed Sensing

Author: Ngoc Ly (SysEn 5800 Fall 2021)

Introduction

$ x \in \mathbb{R}^N $often not really sparse but approximately sparse

$ \Phi \in \mathbb{R}^{M \times N} $for i$ M \ll N $s a Random Gaussian or Bernoulli matrix

$ y \in \mathbb{R}^M $are the observed y samples

$ e \in \mathbb{R}^M $noise vector $ \| e \|_2 \leq \eta $k e k 2 ≤ η

s.t.

How can we reconstruct x from $ y = \Phi x + e $

The goal is to reconstruct $ x \in \mathbb{R}^N $given $ y $ and $ \Phi $

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

let $ [ N ] = \{ 1, \dots , N \} $be an index set $ [N] $ enumerates the columns of $ \Phi $ and $ x $ $ \Phi $ is an under determined systems with infinite solutions. Why $ l_2 $ norm does not work

What is compression is synonymous with to the sparsity.

The problem formulation is to recover sparse data $ \mathbf{x} \in \mathbb{R}^N $

The support of $ \mathbf{x} $ is $ supp(\mathbf{x}) = \{i \in [N] : \mathbf{x}_i \neq 0 \} $ we say $ \mathbf{x} $ is $ k $ sparse when $ |supp(x)| \leq k $

We are interested in the smallest $ supp(x) $ , i.e. $ min(supp(x)) $

Before we get into RIP lets talk about RIC

Restricted Isometry Constant (RIC) is the smallest $ \delta_{|s} \ s.t. \ s \subseteq [N] $that satisfies the RIP condition introduced by Candes, Tao

Random Gaussian and Bernoulli satisfies RIP

Let $ \Phi \in \mathbb{R}^{M \times N} $ satisfy RIP, Let $ [N] $ be an index set For $ s $ is a restriction on $ \mathbf{x} $ denoted by $ x_{|s} $ $ x \in \mathbb{R}^N $ to $ s $ k-sparse $ \mathbf{x} $ 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

�

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 $ \Phi, \mathbf{y}, \mathbf{e} \ \mbox{with} \ \mathbf{y} = \mathbf{\Phi} \mathbf{x} | \mathbf{e} and \mathfrak{M} $

• output $ IHT(\mathbf{y}, \mathbf{\Phi}, \mathfrak{M}) $

• While halting criterion false do
• $ x^{(n+1)} \leftarrow \P_{\mathfrak{M} \left[ x^{(n) + \Phi^* (\mathbf{y} - \mathbf{\Phi x}^{(n)})}\right]} $
• $ n \leftarrow n + 1 $

end while return: $ IHT(\mathbf{y}, \mathbf{\Phi}, \mathfrak{M}) \leftarrow \mathbf{x}^{(n)} $

Numerical Example

Iterative Hard Thresholding IHT