Image Adaptive Reconstruction Based on Compressive Sensing via CoSaMP

Compressive Sampling Matching Pursuit (CoSaMP) is a new iterative recovery algorithm which has splendid theoretical guarantees for convergence and delivers the same guarantees as the best optimization-based approaches. In this paper, we propose a new signal recovery framework which combines the CoSaMP and the genetic algorithm (GA) for better performance. In classic CoSaMP, the number of iterations is fixed. We discuss a new stopping rule to halting the algorithm in this paper. In addition, the choice of several adjustable parameters in the algorithm such as the number of measurements and the sparse level of the signal also will impact the performance. So we gain above parameters via the GA and a large number of experiments. The experiment shows that the new method not only has better recovery quality and higher PSNRs, but also can effectively avoid the premature convergence problem and achieve optimization steadily.