We consider the minimization of a smooth convex function regularized by the mixture of prior models. This problem is generally dif?cult to solve even each simpler regularization problem is easy. In this paper, we present two algorithms to effectively solve it. First, the original problem is decomposed into multiple simpler subproblems. Then, these subproblems are ef?ciently solved by existing techniques in parallel. Finally, the result of the original problem is obtained from the weighted average of solutions of subproblems in an iterative framework. We successfully applied the proposed algorithms to compressed MR image reconstruction and low-rank tensor completion. Numerous experiments demonstrate the superior performance of the proposed algorithm in terms of both the accuracy and computational complexity.

Publications

• Junzhou Huang, Shaoting Zhang and Dimitris Metaxas. " Fast Optimization for Mixture Prior Models ", In Proc. of the 11th European Conference on Computer Vision, ECCV'2010, Crete, Greece, September, 2010.