Phase retrieval is the problem of reconstructing images from magnitude-only measurements. In many real-world applications the problem is underdetermined. When training data is available, generative models allow optimization in a lower-dimensional latent space, hereby constraining the solution set to those images that can be synthesized by the generative model. However, not all possible solutions are within the range of the generator. Instead, they are represented with some error. To reduce this representation error in the context of phase retrieval, we first leverage a novel variation of intermediate layer optimization (ILO) to extend the range of the generator while still producing images consistent with the training data. Second, we introduce new initialization schemes that further improve the quality of the reconstruction. With extensive experiments on the Fourier phase retrieval problem and thorough ablation studies, we can show the benefits of our modified ILO and the new initialization schemes. Additionally, we analyze the performance of our approach on the Gaussian phase retrieval problem.

翻译：阶段检索是从量度测量中重建图像的问题。 在许多现实世界的应用中,问题被低估了。 当有培训数据时, 基因模型允许在低维潜层空间优化, 从而限制给这些图像设置的解决方案, 这些图像可以通过基因模型合成。 但是, 并非所有可能的解决方案都在生成器的范围内。 相反, 它们代表着一些错误 。 为了在阶段检索中减少这个代表错误, 我们首先利用中间层优化( ILO) 的新变异来扩大生成器的范围, 同时继续生成与培训数据一致的图像 。 其次, 我们引入新的初始化计划, 进一步提高重建的质量 。 通过对四级阶段检索问题的广泛实验和彻底的模拟研究, 我们可以展示我们修改后的劳工组织和新的初始化计划的好处 。 此外, 我们分析我们关于高斯阶段检索问题的方法的绩效 。