The phase information of the recorded measurements is lost in the coded diffraction imaging system. However

the phase contains most of structural information about the image. How to reconstruct the original image from measurements without phase information is a crucial problem faced by the phase retrieval algorithms. The optimal parameters of the Gaussian mixture model (GMM) are trained by the expectation maximization (EM) algorithm. An image patch can be represented optimally by one of the components in the GMM model. Based on this fact

a PR optimization problem which fuses the statistical properties of GMM and the data fidelity term is formulated. Moreover

the accelerated proximal gradient method is utilized to solve this problem. The experimental results show that the proposed method can achieve high-quality image at the case of few coded diffraction patterns and high noise levels.