Tuesday, December 24, 2013

Non-convex compressive sensing for X-ray CT: an algorithm comparison

A different kind of phase transition can be seen in the figure above, either the solver fails or it breaks. When it breaks, it means the solver found a sparser solution given the measurements. : Nonconvex compressive sensing for X-ray CT: an algorithm comparison by Rick Chartrand, Emil Y. Sidky, Xiaochuan Pan
Compressive sensing makes it possible to reconstruct images from severely underdetermined linear systems. For X-ray CT, this can allow high-quality images to be reconstructed from projections along few angles, reducing patient dose, as well as enable other forms of limited -view tomography such as tomosynthesis. Many previous results have shown that using nonconvex optimization can greatly improve the results obtained from compressive sensing, and several efficient algorithms have been developed for this purpose. In this paper, we examine some recent algorithms for CT image reconstruction that solve non-convex optimization problems, and compare their reconstruction performance and computational efficiency.

Join the CompressiveSensing subreddit or the Google+ Community and post there !
Liked this entry ? subscribe to Nuit Blanche's feed, there's more where that came from. You can also subscribe to Nuit Blanche by Email, explore the Big Picture in Compressive Sensing or the Matrix Factorization Jungle and join the conversations on compressive sensing, advanced matrix factorization and calibration issues on Linkedin.

No comments: