A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
Extensions of compressed sensing
Signal Processing - Sparse approximations in signal and image processing
Multivariate Statistical Models for Image Denoising in the Wavelet Domain
International Journal of Computer Vision
Exploiting structure in wavelet-based Bayesian compressive sensing
IEEE Transactions on Signal Processing
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
SIAM Journal on Imaging Sciences
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
SIAM Journal on Imaging Sciences
Model-based compressive sensing
IEEE Transactions on Information Theory
Average case analysis of multichannel sparse recovery using convex relaxation
IEEE Transactions on Information Theory
Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency
IEEE Transactions on Signal Processing
Theoretical Results on Sparse Representations of Multiple-Measurement Vectors
IEEE Transactions on Signal Processing
Wavelet-based statistical signal processing using hidden Markovmodels
IEEE Transactions on Signal Processing
Matching pursuits with time-frequency dictionaries
IEEE Transactions on Signal Processing
Reduce and Boost: Recovering Arbitrary Sets of Jointly Sparse Vectors
IEEE Transactions on Signal Processing - Part I
Sparse solutions to linear inverse problems with multiple measurement vectors
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
IEEE Transactions on Information Theory
Image denoising using scale mixtures of Gaussians in the wavelet domain
IEEE Transactions on Image Processing
Rank Awareness in Joint Sparse Recovery
IEEE Transactions on Information Theory
IEEE Transactions on Image Processing
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The prior information of images plays an important role in reducing the computational complexity of CS inversion and improving the reconstruction quality. A wavelet-based multivariate pursuit algorithm, which exploits the prior information of images that goes beyond simple sparsity, is developed in this paper. The proposed method reconstructs the image wavelet coefficients from the multiple measurements in a multivariate manner, and uses the extracted image edge as the prior information to guide the pursuit process of algorithm in CS recovery. By means of the interaction of edge information and multivariate joint recovery, the proposed algorithm significantly improves the reconstruction quality of those images with the obvious edges and high sparsity, such as CT, MRI images. Numerical experiments demonstrate that the proposed algorithm returns superior reconstructed quality and remains higher computational efficiency than other state-of-the-art CS algorithms.