Condy: Ultra-fast high performance restoration using multi-frame L2-relaxed-L0 sparsity and constrained dynamic heuristics

Recently we proposed an efficient technique based on analysis-based sparsity in tight frames to restore images affected by linear blur and additive white Gaussian noise. Such technique performed a joint optimization of a cost function in the image and coefficient space by alternating their corresponding marginal minimizations, until convergence. Here we propose, first, a more standard Bayesian (MAP) re-formulation of the model/method, yielding the same cost function. Second, we propose a heuristical twist in the method, consisting of running only a few iterations (too few to reach convergence), with a dynamic shrinkage plan obtained by maximizing a performance measure in a set of deblurring tests. Third, we propose a multi-frame prior scheme allowing for different thresholds in different frames. Compared to the original method, we achieve a very important speed-up, while keeping high robustness and state-of-the-art performance.