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.
@article{arxiv.2404.06657,
title = {Res-U2Net: Untrained Deep Learning for Phase Retrieval and Image Reconstruction},
author = {Carlos Osorio Quero and Daniel Leykam and Irving Rondon Ojeda},
journal= {arXiv preprint arXiv:2404.06657},
year = {2024}
}