Image compressive sensing reconstruction via group sparse representation and weighted total variation

The traditional compression sensing (CS) reconstruction algorithm based on group sparse representation (GSR) uses the sparsity and nonlocal similarity of the signal to reconstruct the image signal. However, the local smoothness of the image is not sufficiently consiclered, which affects the reconstruction performance of the algorithm. Considering the three prior informations of signal sparsity, nonlocal similarity and smoothness, an image CS reconstruction algorithm based on GSR and weighted total variation (WTV) is proposed. Aiming at the problem that global weighting will introduce wrong texture and edge artifacts for the traditional WTV, a new WTV strategy only sets the weight of high frequency cmponet of image is used to protect the image reconstruction quality. In addition, aiming at the problem that the hard threshold iteration ignores the low frequency principal component coefficient, the hard threshold modulus square method is used to better protect the non-principal component coefficient. Experimental results show that the peak signal to noise ratio of the proposed algorithm is improved 5.4 dB and 0.62 dB compared with the total variation nonlocal regularization and CS algorithm based on GSR at the same sampling rate respectively, which proves that the proposed algorithm effectively protects the details of the image. © 2020, Editorial Office of Systems Engineering and Electronics. All right reserved.