End-to-end Multi-source Image Phase Retrieval

The phase information of multi-source hybrid images is prone to confusion during image propagation. Since traditional phase retrieval algorithms require a large number of assumptions that cannot be executed by obtaining amplitude, which makes phase retrieval of multi-source mixed images with phase distortion challenging. To address these phase retrieval problems, inspired by dual learning, we propose an end-to-end phase retrieval algorithm based on Multi-source Image Phase Retrieval via dual Generative Adversarial Networks (MPRDGAN). By introducing the Least Square Error in the loss function of the network, the algorithm improves the performance of the model to prevent overfitting and ensures the phase retrieval of multi-source mixed images. Experimental results show that the MPRDGAN algorithm reduces the image restoration error, so that the correlation of restored images can reach more than 94.5%, which is widely used in various images such as face and remote sensing.