Hierarchical Estimation and Segmentation of Dense Motion Fields
International Journal of Computer Vision
A Self-Referencing Level-Set Method for Image Reconstruction from Sparse Fourier Samples
International Journal of Computer Vision
A half-quadratic block-coordinate descent method for spectral estimation
Signal Processing
A majorization-minimization algorithm for (multiple) hyperparameter learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Edge-Preserving Image Reconstruction with Wavelet-Domain Edge Continuation
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Accelerating MR image reconstruction on GPUs
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Adaptive wavelet-Galerkin methods for limited angle tomography
Image and Vision Computing
A hybrid Kaczmarz-Conjugate Gradient algorithm for image reconstruction
Mathematics and Computers in Simulation
Enhancement of coupled multichannel images using sparsity constraints
IEEE Transactions on Image Processing
Image reconstruction by an alternating minimisation
Neurocomputing
Numerical Algorithms for Polyenergetic Digital Breast Tomosynthesis Reconstruction
SIAM Journal on Imaging Sciences
Optimization for limited angle tomography in medical image processing
Pattern Recognition
Optimization by Stochastic Continuation
SIAM Journal on Imaging Sciences
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
SNARK09 - A software package for reconstruction of 2D images from 1D projections
Computer Methods and Programs in Biomedicine
Journal of Parallel and Distributed Computing
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We introduce a generalization of a deterministic relaxation algorithm for edge-preserving regularization in linear inverse problems. This algorithm transforms the original (possibly nonconvex) optimization problem into a sequence of quadratic optimization problems, and has been shown to converge under certain conditions when the original cost functional being minimized is strictly convex. We prove that our more general algorithm is globally convergent (i.e., converges to a local minimum from any initialization) under less restrictive conditions, even when the original cost functional is nonconvex. We apply this algorithm to tomographic reconstruction from limited-angle data by formulating the problem as one of regularized least-squares optimization. The results demonstrate that the constraint of piecewise smoothness, applied through the use of edge-preserving regularization, can provide excellent limited-angle tomographic reconstructions. Two edge-preserving regularizers-one convex, the other nonconvex-are used in numerous simulations to demonstrate the effectiveness of the algorithm under various limited-angle scenarios, and to explore how factors, such as the choice of error norm, angular sampling rate and amount of noise, affect the reconstruction quality and algorithm performance. These simulation results show that for this application, the nonconvex regularizer produces consistently superior results