The Application of Wavelet-Based Contourlet Transform on Compressed Sensing

摘要

Reasonable sparse representation of signals are one of the key factors to ensure the quality of compressed sampling, so a proper sparse representing methods should be selected to make the signals sparse to the greatest extent in the applications of compressed sensing. In this paper we employed the wavelet-based contourlet transform, an improved contourlet transform, to decompose 2D images, adopted the framework of block compressed sensing to sample the images, and used the iterative hard thresholding algorithm to reconstruct the original images. Numerical experiments indicated that the runtime of the reconstruction algorithm adopting wavelet-based contourlet transform is the shortest compared to that adopting contourlet transform and that adopting wavelet transform; under low compression ratios, the quality of the reconstructed images using wavelet-based contourlet transform is superior to that using contourlet transform and that using traditional wavelet transform.