Res-U2Net: Untrained Deep Learning for Phase Retrieval and Image Reconstruction
<|reference_start|>Res-U2Net: Untrained Deep Learning for Phase Retrieval and Image Reconstruction: 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.<|reference_end|>
@article{quero2024res-u2net:,
    title={Res-U2Net: Untrained Deep Learning for Phase Retrieval and Image
  Reconstruction},
    author={Carlos Osorio Quero, Daniel Leykam, and Irving Rondon Ojeda},
    journal={Journal of the Optical Society of America A, Vol. 41, Issue 5, pp.
  766-773 (2024)},
    year={2024},
    doi={10.1364/JOSAA.511074},
    archivePrefix={arXiv},
    eprint={2404.06657},
    primaryClass={eess.IV cs.CV physics.app-ph physics.optics}
}