Phase retrieval based on the distributed conditional generative adversarial network

Abstract

Phase retrieval is about reconstructing original vectors/images from their Fourier intensity measurements. Deep learning methods have been introduced to solve the phase retrieval problem; however, most of the proposed approaches cannot improve the reconstruction quality of phase and amplitude of original images simultaneously. In this paper, we present a distributed amplitude and phase conditional generative adversarial network (D-APUCGAN) to achieve the high quality of phase and amplitude images at the same time. D-APUCGAN includes UCGAN, AUCGAN/PUCGAN, and APUCGAN. In this paper, we introduce the content loss function to constrain the similarity between the reconstructed image and the source image through the Frobenius norm and the total variation modulus. The proposed method promotes the quality of phase images better than just using amplitude images to train. The numerical experimental results show that the proposed cascade strategies are significantly effective and remarkable for natural and unnatural images, DIV2K testing datasets, MNIST dataset, and realistic data. Comparing with the conventional neural network methods, the evaluation metrics of PSNR and SSIM values in the proposed method are refined by about 2.25 dB and 0.18 at least, respectively.