Visual reconstruction and the GNC algorithm
Parallel Architectures and Computer Vision
A Fast Algorithm for Edge-Preserving Variational Multichannel Image Restoration
SIAM Journal on Imaging Sciences
An affine scaling methodology for best basis selection
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
Randomization of data acquisition and l1-optimization (recognition with compression)
Automation and Remote Control
Efficient MR image reconstruction for compressed MR imaging
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
Fast image deconvolution using closed-form thresholding formulas of Lq(q=12,23) regularization
Journal of Visual Communication and Image Representation
An Efficient Algorithm for l0 Minimization in Wavelet Frame Based Image Restoration
Journal of Scientific Computing
A fast algorithm for nonconvex approaches to sparse recovery problems
Signal Processing
Editor's Choice Article: Sparse feature selection based on graph Laplacian for web image annotation
Image and Vision Computing
Discrete Tomography in MRI: a Simulation Study
Fundamenta Informaticae - Strategies for Tomography
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Compressive sensing is the reconstruction of sparse images or signals from very few samples, by means of solving a tractable optimization problem. In the context of MRI, this can allow reconstruction from many fewer k-space samples, thereby reducing scanning time. Previous work has shown that nonconvex optimization reduces still further the number of samples required for reconstruction, while still being tractable. In this work, we extend recent Fourier-based algorithms for convex optimization to the nonconvex setting, and obtain methods that combine the reconstruction abilities of previous nonconvex approaches with the computational speed of state-of-the-art convex methods.