Phase Retrieval Algorithm Fusing Multiple Wavelets and Total Variation Regularization

In process of phase retrieval, the image reconstruction quality can be improved when we use the image sparsity as the prior knowledge. By combining the group sparsity of image in wavelet domain with the gradient sparsity of the image itself, we propose a phase retrieval algorithm fusing orthogonal wavelet db10, sym4 group sparsity and total variation regularization for the coded diffraction pattern model. Aiming at the problem that reconfiguration time of the current phase retrieval algorithm is long, we use composite splitting algorithm to decompose nonconvex optimization problem into several sub-problems (including two group hard threshold operators and total variation minimization) that can be solved easily, which reduces the image reconstruction time. Experimental results show that the peak signal-to-noise ratio of the reconstructed image obtained by the proposed algorithm is improved by about 0.8 dB compared with that of BM3D-PRGAMP algorithm under Gaussian noise, and the reconstruction time is reduced by 90%. In Poisson model, the proposed algorithm also has a great advantage, which fully demonstrates that the algorithm is robust to noise.