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Sparse Approximation by Wavelet Frames and Applications Bin Dong Department of Mathematics The University of Arizona 2012 International Workshop on Signal.

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Presentation on theme: "Sparse Approximation by Wavelet Frames and Applications Bin Dong Department of Mathematics The University of Arizona 2012 International Workshop on Signal."— Presentation transcript:

1 Sparse Approximation by Wavelet Frames and Applications Bin Dong Department of Mathematics The University of Arizona 2012 International Workshop on Signal Processing, Optimization, and Control June 30- July 3, 2012 USTC, Hefei, Anhui, China

2 Outlines I. Wavelet Frame Based Models for Linear Inverse Problems (Image Restoration) II. Applications in CT Reconstruction  1-norm based models  Connections to variational model  0-norm based model  Comparisons: 1-norm v.s. 0-norm  Quick Intro of Conventional CT Reconstruction  CT Reconstruction with Radon Domain Inpainting

3 Tight Frames in  Orthonormal basis  Riesz basis  Tight frame: Mercedes-Benz frame  Expansions: Unique Not unique

4 Tight Frames  General tight frame systems  Tight wavelet frames  Construction of tight frame: unitary extension principles [Ron and Shen, 1997] They are redundant systems satisfying Parseval’s identity Or equivalently where and

5 Tight Frames  Example:  Fast transforms  Lecture notes: [Dong and Shen, MRA-Based Wavelet Frames and Applications, IAS Lecture Notes Series,2011]  Decomposition  Reconstruction  Perfect Reconstruction  Redundancy

6 Image Restoration Model  Image Restoration Problems  Challenges: large-scale & ill-posed Denoising, when is identity operator Deblurring, when is some blurring operator Inpainting, when is some restriction operator CT/MR Imaging, when is partial Radon/Fourier transform

7 Frame Based Models  Image restoration model:  Balanced model for image restoration [Chan, Chan, Shen and Shen, 2003], [Cai, Chan and Shen, 2008]  When, we have synthesis based model [Daubechies, Defrise and De Mol, 2004; Daubechies, Teschke and Vese, 2007]  When, we have analysis based model [Stark, Elad and Donoho, 2005; Cai, Osher and Shen, 2009] Resembles Variational Models

8 Connections: Wavelet Transform and Differential Operators  Nonlinear diffusion and iterative wavelet and wavelet frame shrinkage o 2 nd -order diffusion and Haar wavelet: [Mrazek, Weickert and Steidl, 2003&2005] o High-order diffusion and tight wavelet frames in 1D: [Jiang, 2011]  Difference operators in wavelet frame transform:  True for general wavelet frames with various vanishing moments [Weickert et al., 2006; Shen and Xu, 2011] Filters Transform Approximation

9 Connections: Analysis Based Model and Variational Model  [Cai, Dong, Osher and Shen, Journal of the AMS, 2012]:  The connections give us  Leads to new applications of wavelet frames: Converges Geometric interpretations of the wavelet frame transform (WFT) WFT provides flexible and good discretization for differential operators Different discretizations affect reconstruction results Good regularization should contain differential operators with varied orders (e.g., total generalized variation [Bredies, Kunisch, and Pock, 2010])  Image segmentation: [Dong, Chien and Shen, 2010]  Surface reconstruction from point clouds: [Dong and Shen, 2011] For any differential operator when proper parameter is chosen. Standard DiscretizationPiecewise Linear WFT

10 Frame Based Models: 0-Norm  Nonconvex analysis based model [Zhang, Dong and Lu, 2011]  Motivations:  Related work: Restricted isometry property (RIP) is not satisfied for many applications Penalizing “norm” of frame coefficients better balances sparsity and smoothnes “norm” with : [Blumensath and Davies, 2008&2009] quasi-norm with : [Chartrand, 2007&2008]

11 Fast Algorithm: 0-Norm  Penalty decomposition (PD) method [Lu and Zhang, 2010]  Algorithm: Change of variables Quadratic penalty

12 Fast Algorithm: 0-Norm  Step 1:  Subproblem 1a): quadratic  Subproblem 1b): hard-thresholding  Convergence Analysis [Zhang, Dong and Lu, 2011] :

13 Numerical Results  Comparisons (Deblurring) Balanced Analysis 0-Norm PFBS/FPC: [Combettes and Wajs, 2006] /[Hale, Yin and Zhang, 2010] Split Bregman: [Goldstein and Osher, 2008] & [Cai, Osher and Shen, 2009] PD Method: [Zhang, Dong and Lu, 2011]

14 Numerical Results  Comparisons Portrait Couple BalancedAnalysis

15 Faster Algorithm: 0-Norm  Start with some fast optimization method for nonsmooth and convex optimizations: doubly augmented Lagrangian (DAL) method [Rockafellar, 1976]. Given the problem: The DAL method: where We solve the joint optimization problem of the DAL method using an inexact alternative optimization scheme

16 Faster Algorithm: 0-Norm  Start with some fast optimization method for nonsmooth and convex optimizations: doubly augmented Lagrangian (DAL) method [Rockafellar, 1976].  The inexact DAL method: Given the problem: The DAL method: where Hard thresholding

17 Faster Algorithm: 0-Norm  However, the inexact DAL method does not seem to converge!! Nonetheless, the sequence oscillates and is bounded.  The mean doubly augmented Lagrangian method (MDAL) [Dong and Zhang, 2011] solve the convergence issue by using arithmetic means of the solution sequence as outputs instead: MDAL:

18 Comparisons: Deblurring  Comparisons of best PSNR values v.s. various noise level

19 Comparisons: Deblurring  Comparisons of computation time v.s. various noise level

20 Comparisons: Deblurring  What makes “lena” so special?  Decay of the magnitudes of the wavelet frame coefficients is very fast, which is what 0-norm prefers.  Similar observation was made earlier by [Wang and Yin, 2010]. 1-norm0-norm: PD0-norm: MDAL

21 APPLICATIONS IN CT RECONSTRUCTION With the Center for Advanced Radiotherapy and Technology (CART), UCSD

22 Cone Beam CT 3D Cone Beam CT

23 Discrete 3D Cone Beam CT =  Animation created by Dr. Xun Jia

24  Goal: solve  Difficulties:  Related work: Cone Beam CT Image Reconstruction Unknown ImageProjected Image In order to reduce dose, the system is highly underdetermined. Hence the solution is not unique. Projected image is noisy.  Total Variation (TV): [Sidkey, Kao and Pan 2006], [Sidkey and Pan, 2008], [Cho et al. 2009], [Jia et al. 2010];  EM-TV: [Yan et al. 2011]; [Chen et al. 2011];  Wavelet Frames: [Jia, Dong, Lou and Jiang, 2011];  Dynamical CT/4D CT: [Chen, Tang and Leng, 2008], [Jia et al. 2010], [Tian et al., 2011]; [Gao et al. 2011];

25 CT Image Reconstruction with Radon Domain Inpainting  Idea: start with  Benefits:  Instead of solving  We find both and such that: is close to but with better quality Prior knowledge of them should be used Safely increase imaging dose Utilizing prior knowledge we have for both CT images and the projected images

26 CT Image Reconstruction with Radon Domain Inpainting  Model [Dong, Li and Shen, 2011]  Algorithm: alternative optimization & split Bregman. where p=1, anisotropic p=2, isotropic

27 CT Image Reconstruction with Radon Domain Inpainting  Algorithm [Dong, Li and Shen, 2011]: block coordinate descend method [Tseng, 2001]  Convergence Analysis Problem: Algorithm: Note: If each subproblem is solved exactly, then the convergence analysis was given by [Tseng, 2001], even for nonconvex problems.

28 CT Image Reconstruction with Radon Domain Inpainting  Results: N denoting number of projections N=15N=20

29 CT Image Reconstruction with Radon Domain Inpainting  Results: N denoting number of projections N=15 N=20 W/O InpaintingWith Inpainting

30 Thank You Collaborators: Mathematics  Stanley Osher, UCLA  Zuowei Shen, NUS  Jia Li, NUS  Jianfeng Cai, University of Iowa  Yifei Lou, UCLA/UCSD  Yong Zhang, Simon Fraser University, Canada  Zhaosong Lu, Simon Fraser University, Canada Medical School  Steve B. Jiang, Radiation Oncology, UCSD  Xun Jia, Radiation Oncology, UCSD  Aichi Chien, Radiology, UCLA


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