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3D refractive index imaging methods usually rely on a weak-scattering approximation that does not allow for thick samples to be imaged accurately. Recent methods such as 3D Fourier ptychographic microscopy (FPM) instead use multiple-scattering models which allow for thicker objects to be imaged. In practice the illumination-side coding of 3D FPM requires redundant information and may produce inaccurate reconstructions for thick samples. Here, we propose augmenting 3D FPM with detection-side coding using a spatial light modulator (SLM) and optimize the SLM coding strategy with physics-based machine learned pupil coding designs that are optimized for 3D reconstructions. We compare our learned designs to random-, defocus-, Zernike aberrations-based pupil codes in simulated and experimental results.
Regina Eckert,Michael R. Kellman, andLaura Waller
"Physics-based learning for measurement diversity in 3D refractive index microscopy (Conference Presentation)", Proc. SPIE 11245, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXVII, 112450X (9 March 2020); https://doi.org/10.1117/12.2543402
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Regina Eckert, Michael R. Kellman, Laura Waller, "Physics-based learning for measurement diversity in 3D refractive index microscopy (Conference Presentation)," Proc. SPIE 11245, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXVII, 112450X (9 March 2020); https://doi.org/10.1117/12.2543402