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Workshop: NeurIPS 2023 Workshop: Machine Learning and the Physical Sciences

LoDIP: Low-dose phase retrieval with deep image prior

Raunak Manekar · Elisa Negrini · Minh Pham · Daniel Jacobs · Jaideep Srivastava · Stanley Osher · Jianwei Miao


Phase retrieval under very low dose conditions is a challenging problem as all the phase retrieval algorithms become unstable with the presence of very high Poisson noise. To mitigate this problem, in-situ coherent diffractive imaging (CDI) has been previously proposed, consisting of a static region of strong scatterers and a dynamic region of a sample. The static region is illuminated by a very high dose, while the dynamic region is irradiated by a very low dose, producing a coherent interference pattern from the two regions. Iterative phase retrieval algorithms are then used to reconstruct both regions from the diffraction patterns with high signal to noise ratio. Numerical simulations have indicated that in-situ CDI can reduce radiation dose by one to two orders of magnitude over conventional CDI. Here we demonstrate low-dose phase retrieval with deep image prior, termed LoDIP, for in-situ CDI. Using both numerical and experimental data, we demonstrate that LoDIP outperfroms popular iterative phase retrieval algorithms under low-dose conditions. Our results show that LoDIP is not sensitive to the choice of the static structure nor to the geometric arrangement between the two objects. Additionally, unlike previous successful work with in situ CDI, LoDIP does not depend on multiple measurements with a common static region. We expect that the combination of deep-learning phase retrieval with in situ CDI will create numerous opportunities for high-resolution quantitative phase imaging for dose-sensitive materials, such as biological samples, polymers, organic semiconductors, and energy materials.

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