Adaptive Image Parallel Compressed Sensing Algorithm Based on Sparsity Fitting

In order to improve the reconstruction accuracy and processing speed of an image,an adaptive wavelet packet image parallel compressed sensing algorithm with sparsity fitting is proposed.First,the sparse transformation was carried out on the image blocks which were of the same size and not overlapped using wavelet packet.An iterative method was used to determine the minimum sampling rate satisfying the accuracy of image reconstruction under the optimal decomposition scale,and the least square method was used to optimize the sampling rate.Then,the algorithm was parallelized with MapReduce framework combined with cloud computing technology.A computer cluster was built in the laboratory under Java development environment,and the compression rate,reconstruction performance and operation time of different algorithms were compared by using standard image as samples.The results show that the reconstruction quality and processing speed of the algorithm are improved significantly.