Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
JPEG 2000: Image Compression Fundamentals, Standards and Practice
JPEG 2000: Image Compression Fundamentals, Standards and Practice
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Extensions of compressed sensing
Signal Processing - Sparse approximations in signal and image processing
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Signal Reconstruction From Noisy Random Projections
IEEE Transactions on Information Theory
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The increased high-resolution capabilities of modem medical image acquisition systems raise the crucial tasks of effectively storing and interacting with large databases of such data. The ease of image storage and query would be unfeasible without compression, which represents high-resolution images with a relatively small set of significant transfonn coefficients. Due to the specific content of medical images, compression often results in highly sparse representations in appropriate orthononnal bases. The inherent property of compressed sensing (CS) working simultaneously as a sensing and compression protocol using a small subset of random projection coefficients, enables a potentially significant reduction in storage requirements. In this paper, we introduce a Bayesian CS approach for obtaining highly sparse representations of medical images based on a set of noisy CS measurements, where the prior beliefthat the vector of transfonn coefficients should be sparse is exploited by modeling its probability distribution by means of a Gaussian Scale Mixture. The experimental results show that the proposed approach maintains the reconstruction perfonnance of other state-of-the-art CS methods while achieving significantly sparser representations of medical images with distinct content.