论文标题
PR-DAD：使用Deep Auto-Decoder的阶段检索
PR-DAD: Phase Retrieval Using Deep Auto-Decoders
论文作者
论文摘要
相位检索是一个众所周知的不良反问题，在该问题中，只有其傅立叶变换的幅度值作为输入，就试图恢复图像。近年来，已经提出了基于深度学习的新算法，提供了超过经典方法结果的突破性结果。在这项工作中，我们提供了一种新颖的深度学习体系结构pr-dad（使用深度自动解码器的相位检索），其组件是基于相位检索问题的数学建模仔细设计的。该体系结构提供了超过所有当前结果的实验结果。
Phase retrieval is a well known ill-posed inverse problem where one tries to recover images given only the magnitude values of their Fourier transform as input. In recent years, new algorithms based on deep learning have been proposed, providing breakthrough results that surpass the results of the classical methods. In this work we provide a novel deep learning architecture PR-DAD (Phase Retrieval Using Deep Auto- Decoders), whose components are carefully designed based on mathematical modeling of the phase retrieval problem. The architecture provides experimental results that surpass all current results.
