Untrained deep learning-based differential phase-contrast microscopy

Ingyoung Kim; Baekcheon Seong; Taegyun Moon; Malith Ranathunga; Daesuk Kim; Chulmin Joo

doi:10.1117/12.3001496

13 March 2024 Untrained deep learning-based differential phase-contrast microscopy

Ingyoung Kim, Baekcheon Seong, Taegyun Moon, Malith Ranathunga, Daesuk Kim, Chulmin Joo

Proceedings Volume PC12857, Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences; PC128571B (2024) https://doi.org/10.1117/12.3001496
Event: SPIE BiOS, 2024, San Francisco, California, United States

Conference Poster

Abstract

Quantitative differential phase-contrast (DPC) microscopy is a viable imaging method that provides phase images of transparent objects by using multiple intensity images. Conventional DPC methods rely on a linearized image formation model that is applicable to weakly scattering objects only, thus limiting the phase range of objects that can be accurately imaged. Additionally, these methods necessitate additional measurements and complex algorithms to correct for system aberrations. In this presentation, we introduce self-calibrated DPC microscopy using an untrained neural network (UNN-DPC) that incorporates a nonlinear image formation model and system aberration. Our method overcomes the limitations imposed by the linearized model and enables the simultaneous reconstruction of complex object information and aberrations without a training dataset.

Citation Download Citation

Ingyoung Kim, Baekcheon Seong, Taegyun Moon, Malith Ranathunga, Daesuk Kim, and Chulmin Joo "Untrained deep learning-based differential phase-contrast microscopy", Proc. SPIE PC12857, Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences, PC128571B (13 March 2024); https://doi.org/10.1117/12.3001496

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