  Conventional deep learning-based image reconstruction methods require a large
amount of training data which can be hard to obtain in practice. Untrained deep
learning methods overcome this limitation by training a network to invert a
physical model of the image formation process. Here we present a novel
untrained Res-U2Net model for phase retrieval. We use the extracted phase
information to determine changes in an object's surface and generate a mesh
representation of its 3D structure. We compare the performance of Res-U2Net
phase retrieval against UNet and U2Net using images from the GDXRAY dataset.
