Efficient Deterministic Compressed Sensing for Images with ...asufrg/files/kni.pdfKang-Yu (Connie)...
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Efficient Deterministic Compressed Sensing for Images with Chirps and Reed-Muller Sequences Kang-Yu (Connie) Ni, Arizona State University, joint with Somantika Datta, Prasun Mahanti, Svetlana Roudenko, Douglas Cochran
Compressed Sensing: Overview
!
" x = y
!
" sensing matrixy data/measurementsx sparse signal
!
n " N n "1
!
N "1
want to recover!
1. : k-sparse 2. : RIP (Restricted Isometry Property), e.g. random matrices 3. Practical reconstruction algorithms, e.g. l1 minimization
*Candes, Romberg, Tao 2006 and Donoho 2006
!
x
!
"
k < n << N
Sensing Matrix: Determinis7c Approach • Why deterministic sensing?
– Explicit reconstruction algorithm – Efficient storage – Smaller error in reconstruction
• Existing works (1d signals) – DeVore – via finite fields – Chirp matrices – Applebaum, Howard, Searle, Calderbank – 2nd-order RM sequences – Howard, Calderbank, Searle, Jafarpour – Indyk, Iwen, Herman, … see http://dsp.rice.edu/cs
• Need a method suitable for images
Sta7s7cal Restricted Isometry Property is -StRIP if for k-sparse , holds with probability exceeding 1-
!
(1"#) x 22$ %x 2
2$ (1+#) x 2
2
!
"
!
"
!
(k, ", #)
!
x " RN
*Calderbank, Howard, Jafarpour, 2010
CS with Chirps and RM Sequences • 2nd-order Reed-Muller functions
!
"P ,b (a) =12m
i2bT a+ Pa( )T a
a, b#$ 2m binary vectors of length m
P : m % m binary symmetric matrix
!
"r,m (l) =1ne2#in ml+ 2#i
n rl 2
r, m, l$% n
• discrete chirp signal
** Howard et al.
• Hadamard matrix
• Reed-Muller matrix (P is zero-diagonal)
m=2! 1 1 1 1 1 -1 1 -1 1 1 -1 -1 1 -1 -1 1
• Fourier matrix
• chirp matrix
n=3!
* Applebaum et al.
!
" = [UP1UP2
! UP2m(m#1) /2
]
2m X 2m (m+1)/2!
!
! " # # # $ # # #
!
" = [Ur1Ur2! Urn
]
n X n2!
!
! " # # # $ # # #
Pros: Outperform MP in recon error and computational complexity – MP – det CS with chirp
Cons: Not suited for images 256 × 256 image with 10% sparsity k = 6,554, N = 65,536 – rule of thumb n ≈ 22,670,
but n ×N = image not sparse enough – least squares problem becomes too large
!
n > k log2(1+ N /k)
Chirp and RM Reconstruc7on Algorithms
2m X 2m (m+1)/2
!
O(knN)
!
O kn2 logn( )
!
Nn
=6553622670
" 2.89
Results n / N Image, Sparsity n / k noiselets chirp RM 25% Brain, 7% 3.6 25.2 dB
123 dB
119 dB
12.5% Vessel, 5% 2.5 10.1 dB
49.9 dB
10.6 dB
6.25% Man, 2.38% 2.6 14.5 dB
112 dB
109 dB
!
" = U1 U2 U3 U4[ ]
!
" = U1 U2 U3 U4 U5 U6 U7 U8[ ]
!
" = U1 U2 U3 U4 U5 U6 U7 U8 U9 U10 U11 U12 U13 U14 U15 U16[ ]
Reconstruc7on Algorithm 0. Perform initial approximation and then get residual Repeat 1 - 3 until residual is sufficiently small
1 Detect support 2 Determine coefficients 3 Get residual
!
y0 = y " A˜ z
Construc7on of Sensing Matrices • N = image size (expl: 512 X 512 = 218 ) • n = N / 4 (expl: 216 ) • Sensing matrix: • Satisfy the Statistical Restricted Isometry Property
!
" = [ U1 #U2 U3 #U4 ]
0. Ini7al Best Approxima7on of Solu7on • Detection of the “bulk” of a signal • Based on energy of wavelets concentrate on upper-left region
!
y ="x = [ U1 U2 U3 U4 ] =U1x1 +U2x2 +U3x3 +U4x4
!
U1*y =U1
*U1x1 +U1*U2x2 +U1
*U3x3 +U1*U4x4 " x1
!
x1
x2
x3
x4
"
#
$ $ $ $
%
&
' ' ' '
Acknowledgement • This work was partially supported by NSF-DMS FRG grant #0652833, NSF-DUE
#0633033, ONR-BRC grant #N00014-08-1-1110 • Robert Calderbank, Sina Jafarpour, Stephen Howard, Stephen Searle – discussions
of their work in deterministic CS. Justin Romberg – advice about noiselets and l1 algorithms. Jim Pipe – guidance about medical imaging, providing MRI images
Email: [email protected]
2. Determine Coefficients by Least Squares
!
A z = y
solved by LSQR [Paige & Saunders]
!
˜ z = argminz
A z " y2
!
Ut = DvtU1
!
n " k
!
SNR(dB) = 10 log10 || xactual ||2 || xactual " xrecon ||2[ ]
• Hard-threshold to obtain a set of locations, denoted by • Let , the initial approx. is
!
U1*y
!
"
!
A =U1 "
!
˜ z = A*y
1. Detect Support by DCFT (or DCHT) • From
• Update
• Let !
w(t, l) =n
DFT y0(l)vt (l){ }, t =1, 2, 3, 4, vt =1st column of Ut
!
" = "# locations associated with d largest w(t, l){ }
!
A ="#
Conclusion • Extend the utility of CS using deterministic matrices • Demonstrate a method that supports imaging applications
Ongoing works: • Investigate more natural formulations for multi-dimensional signals • Exploit deterministic CS with a priori knowledge on signals
noiselets: random noiselet measurements* with l1 minimization** *Candes and Romberg, sparsity and incoherence in compressive sampling **Zhang, Yang, and Yin, YALL1: Your ALgorithms for L1
!
y = " x + µ
!
µ : noise with standard deviation "
n / N Image σ noiselets chirp RM 25% Brain 0
0.05 0.1
23.4 dB 16.5 dB 12.5 dB
28.4 dB 25.2 dB 21.3 dB
25.7 dB 24.9 dB 20.9 dB
25% Vessel 0 0.05 0.1
12.0 dB 6.9 dB 2.6 dB
14.1 dB 12.4 dB 9.9 dB
13.4 dB 12.3 dB 9.1 dB
25% Man 0 0.05 0.1
20.0 dB 16.2 dB 12.7 dB
23.2 dB 22.5 dB 20.2 dB
22.6 dB 21.7 dB 19.4 dB
!
x : compressible (not sparsified)
!
1 1 1
1 e2"i3 1#1 e
2"i3 2#1
1 e2"i3 1#2 e
2"i3 2#2
$
%
& & & &
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(
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=:U0