Res-U2Net: Untrained Deep Learning for Phase Retrieval and Image Reconstruction

• URL: http://arxiv.org/abs/2404.06657v1
• Date: Tue, 9 Apr 2024 23:47:53 GMT
• Title: Res-U2Net: Untrained Deep Learning for Phase Retrieval and Image Reconstruction
• Authors: Carlos Osorio Quero, Daniel Leykam, Irving Rondon Ojeda,
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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