Abstract:

The signal recovery performance of compressed sensing (CS) requires that the cross correlations between the measurement matrix and sparse transformed matrix should be as small as possible. In order to reduce the cross correlations, an varied step gradient descent algorithm is studied and simulated annealing (SA) learning rate factor is introduced to adjust the step function. The simulation results demonstrate that due to the adaptive adjustment of step length in the iteration process, the speed of optimizing matrix is fast, more mutual coherence coefficients are distributed around zero, and the peak signal to noise ratio of reconstructed image is improved with the optimized measurement matrix. The improved algorithm has good performance in achieving lower mutual coherence and improving reconstruction performance.