Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Variational Relevance Vector Machines
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Extensions of compressed sensing
Signal Processing - Sparse approximations in signal and image processing
Multi-task compressive sensing with Dirichlet process priors
Proceedings of the 25th international conference on Machine learning
Compressed sensing and Bayesian experimental design
Proceedings of the 25th international conference on Machine learning
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
IEEE Transactions on Signal Processing
Sampling theorems for signals from the union of finite-dimensional linear subspaces
IEEE Transactions on Information Theory
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Model-based compressive sensing
IEEE Transactions on Information Theory
Wavelet-based statistical signal processing using hidden Markovmodels
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
IEEE Transactions on Information Theory
Image denoising using scale mixtures of Gaussians in the wavelet domain
IEEE Transactions on Image Processing
A new, fast, and efficient image codec based on set partitioning in hierarchical trees
IEEE Transactions on Circuits and Systems for Video Technology
Bayesian compressive sensing via belief propagation
IEEE Transactions on Signal Processing
Model-based compressive sensing
IEEE Transactions on Information Theory
IEEE Transactions on Signal Processing
Proximal Methods for Hierarchical Sparse Coding
The Journal of Machine Learning Research
Bayesian compressive sensing for cluster structured sparse signals
Signal Processing
Convex and Network Flow Optimization for Structured Sparsity
The Journal of Machine Learning Research
Structured Variable Selection with Sparsity-Inducing Norms
The Journal of Machine Learning Research
Learning with Structured Sparsity
The Journal of Machine Learning Research
Pattern Recognition Letters
Extracting non-negative basis images using pixel dispersion penalty
Pattern Recognition
Block-Based Compressed Sensing of Images and Video
Foundations and Trends in Signal Processing
Dictionary Learning for Noisy and Incomplete Hyperspectral Images
SIAM Journal on Imaging Sciences
Adaptive Compressed Image Sensing Using Dictionaries
SIAM Journal on Imaging Sciences
A learning-based method for compressive image recovery
Journal of Visual Communication and Image Representation
Image representation using block compressive sensing for compression applications
Journal of Visual Communication and Image Representation
| Hi-index | 35.75 |
Bayesian compressive sensing (CS) is considered for signals and images that are sparse in a wavelet basis. The statistical structure of the wavelet coefficients is exploited explicitly in the proposed model, and, therefore, this framework goes beyond simply assuming that the data are compressible in a wavelet basis. The structure exploited within the wavelet coefficients is consistent with that used in wavelet-based compression algorithms. A hierarchical Bayesian model is constituted, with efficient inference via Markov chain Monte Carlo (MCMC) sampling. The algorithm is fully developed and demonstrated using several natural images, with performance comparisons to many state-of-the-art compressive-sensing inversion algorithms.