arXiv subjects

Jeffrey A. Fessler

Publications and source records attributed to Jeffrey A. Fessler.

78 records · Page 5Linked to original sources

arXiv2015

Relaxed Linearized Algorithms for Faster X-Ray CT Image Reconstruction

Statistical image reconstruction (SIR) methods are studied extensively for X-ray computed tomography (CT) due to the potential of acquiring CT scans with reduced X-ray dose while maintaining image quality. However, the longer reconstruction time of SIR methods hinders their use in X-ray CT in practice. To accelerate statistical methods, many optimization techniques have been investigated. Over-relaxation is a common technique to speed up convergence of iterative algorithms. For instance, using a relaxation parameter that is close to two in alternating direction method of multipliers (ADMM) has been shown to speed up convergence significantly. This paper proposes a relaxed linearized augmented Lagrangian (AL) method that shows theoretical faster convergence rate with over-relaxation and applies the proposed relaxed linearized AL method to X-ray CT image reconstruction problems. Experimental results with both simulated and real CT scan data show that the proposed relaxed algorithm (with ordered-subsets [OS] acceleration) is about twice as fast as the existing unrelaxed fast algorithms, with negligible computation and memory overhead.

math.OC↗

arXiv2015

Algorithmic Design of Majorizers for Large-Scale Inverse Problems

Iterative majorize-minimize (MM) (also called optimization transfer) algorithms solve challenging numerical optimization problems by solving a series of "easier" optimization problems that are constructed to guarantee monotonic descent of the cost function. Many MM algorithms replace a computationally expensive Hessian matrix with another more computationally convenient majorizing matrix. These majorizing matrices are often generated using various matrix inequalities, and consequently the set of available majorizers is limited to structures for which these matrix inequalities can be efficiently applied. In this paper, we present a technique to algorithmically design matrix majorizers with wide varieties of structures. We use a novel duality-based approach to avoid the high computational and memory costs of standard semidefinite programming techniques. We present some preliminary results for 2D X-ray CT reconstruction that indicate these more exotic regularizers may significantly accelerate MM algorithms.

stat.CO↗

arXiv2014

Undersampled Phase Retrieval with Outliers

We propose a general framework for reconstructing transform-sparse images from undersampled (squared)-magnitude data corrupted with outliers. This framework is implemented using a multi-layered approach, combining multiple initializations (to address the nonconvexity of the phase retrieval problem), repeated minimization of a convex majorizer (surrogate for a nonconvex objective function), and iterative optimization using the alternating directions method of multipliers. Exploiting the generality of this framework, we investigate using a Laplace measurement noise model better adapted to outliers present in the data than the conventional Gaussian noise model. Using simulations, we explore the sensitivity of the method to both the regularization and penalty parameters. We include 1D Monte Carlo and 2D image reconstruction comparisons with alternative phase retrieval algorithms. The results suggest the proposed method, with the Laplace noise model, both increases the likelihood of correct support recovery and reduces the mean squared error from measurements containing outliers. We also describe exciting extensions made possible by the generality of the proposed framework, including regularization using analysis-form sparsity priors that are incompatible with many existing approaches.

stat.AP↗

arXiv2014

A convergence proof of the split Bregman method for regularized least-squares problems

The split Bregman (SB) method [T. Goldstein and S. Osher, SIAM J. Imaging Sci., 2 (2009), pp. 323-43] is a fast splitting-based algorithm that solves image reconstruction problems with general l1, e.g., total-variation (TV) and compressed sensing (CS), regularizations by introducing a single variable split to decouple the data-fitting term and the regularization term, yielding simple subproblems that are separable (or partially separable) and easy to minimize. Several convergence proofs have been proposed, and these proofs either impose a "full column rank" assumption to the split or assume exact updates in all subproblems. However, these assumptions are impractical in many applications such as the X-ray computed tomography (CT) image reconstructions, where the inner least-squares problem usually cannot be solved efficiently due to the highly shift-variant Hessian. In this paper, we show that when the data-fitting term is quadratic, the SB method is a convergent alternating direction method of multipliers (ADMM), and a straightforward convergence proof with inexact updates is given using [J. Eckstein and D. P. Bertsekas, Mathematical Programming, 55 (1992), pp. 293-318, Theorem 8]. Furthermore, since the SB method is just a special case of an ADMM algorithm, it seems likely that the ADMM algorithm will be faster than the SB method if the augmented Largangian (AL) penalty parameters are selected appropriately. To have a concrete example, we conduct a convergence rate analysis of the ADMM algorithm using two splits for image restoration problems with quadratic data-fitting term and regularization term. According to our analysis, we can show that the two-split ADMM algorithm can be faster than the SB method if the AL penalty parameter of the SB method is suboptimal. Numerical experiments were conducted to verify our analysis.

math.OC↗

arXiv2014

Fast X-ray CT image reconstruction using the linearized augmented Lagrangian method with ordered subsets

The augmented Lagrangian (AL) method that solves convex optimization problems with linear constraints has drawn more attention recently in imaging applications due to its decomposable structure for composite cost functions and empirical fast convergence rate under weak conditions. However, for problems such as X-ray computed tomography (CT) image reconstruction and large-scale sparse regression with "big data", where there is no efficient way to solve the inner least-squares problem, the AL method can be slow due to the inevitable iterative inner updates. In this paper, we focus on solving regularized (weighted) least-squares problems using a linearized variant of the AL method that replaces the quadratic AL penalty term in the scaled augmented Lagrangian with its separable quadratic surrogate (SQS) function, thus leading to a much simpler ordered-subsets (OS) accelerable splitting-based algorithm, OS-LALM, for X-ray CT image reconstruction. To further accelerate the proposed algorithm, we use a second-order recursive system analysis to design a deterministic downward continuation approach that avoids tedious parameter tuning and provides fast convergence. Experimental results show that the proposed algorithm significantly accelerates the "convergence" of X-ray CT image reconstruction with negligible overhead and greatly reduces the OS artifacts in the reconstructed image when using many subsets for OS acceleration.

math.OC↗

arXiv2003

Baseband Detection of Bistatic Electron Spin Signals in Magnetic Resonance Force Microscopy (MRFM)

In single spin Magnetic Resonance Force Microscopy (MRFM), the objective is to detect the presence of an electron (or nuclear) spin in a sample volume by measuring spin-induced attonewton forces using a micromachined cantilever. In the OSCAR method of single spin MRFM, the spins are manipulated by an external rf field to produce small periodic deviations in the resonant frequency of the cantilever. These deviations can be detected by frequency demodulation followed by conventional amplitude or energy detection. In this paper, we present an alternative to these detection methods, based on optimal detection theory and Gibbs sampling. On the basis of simulations, we show that our detector outperforms the conventional amplitude and energy detectors for realistic MRFM operating conditions. For example, to achieve a 10% false alarm rate and an 80% correct detection rate our detector has an 8 dB SNR advantage as compared with the conventional amplitude or energy detectors. Furthermore, at these detection rates it comes within 4 dB of the omniscient matched-filter lower bound.

quant-ph↗