This paper develops an FBP-MAP (Filtered Backprojection, Maximum a Posteriori) algorithm to reconstruct MRI images from under-sampled data. An objective function is first set up for the MRI reconstruction problem with a data fidelity term and a Bayesian term. The Bayesian term is a constraint in the temporal dimension. This objective function is minimized using the calculus of variations. The p...

in this paper we introduce a sequential block iterative method and its simultaneous version with op-timal combination of weights (instead of convex combination) for solving convex feasibility problems.when the intersection of the given family of convex sets is nonempty, it is shown that any sequencegenerated by the given algorithms converges to a feasible point. additionally for linear feasibil...

For lossy image compression systems, we develop an algorithm called iterative refinement, to improve the decoder’s reconstruction compared with standard decoding techniques. Specifically, we propose a recurrent neural network approach for nonlinear, iterative decoding. Our neural decoder, which can work with any encoder, employs self-connected memory units that make use of both causal and non-c...

Optical diffusion tomography is a method for reconstructing three-dimensional optical properties from light that passes through a highly scattering medium. Computing reconstructions from such data requires the solution of a nonlinear inverse problem. The situation is further complicated by the fact that while reconstruction algorithms typically assume exact knowledge of the optical source and d...

Model-based iterative reconstruction (MBIR) techniques have demonstrated many advantages in X-ray CT image reconstruction. The tuning parameter value in MBIR that regulates the strength of the penalty function is critical for achieving good reconstruction results but difficult to choose. The path-based iterative reconstruction (PBIR) method empowered by the path seeking algorithm is capable of ...