Smart Computational Imaging (SCI) Lab

The first attempt of translating "computational microscopy" from concept to stand-alone optical instruments, and the work is published in Photonix
The Bayesian convolutional neural network not only retrieves the phase from a single fringe pattern but also predicts uncertainties that depict the pixel-wise confidence measure of its estimation.
Deep learning is currently prompting increasing research interests and leading to a paradigm shift from physics-based modeling to data-driven learning in the field of optical metrology
We present an overview of fringe projection techniques, as well as a new and definitive classification for them,  and discuss the advantages and constraints of the techniques.
We reported a realtime microscopic 3D measurememnt technique that realizes real-time 3D measurement
at 120 frames per second (FPS) with the accuracy of 8 μm.
DPC with optimal illumination scheme reconstructs quantitative phase distribution of Hela cells in vitro with isotropic resolution.
REFPM endows a 10×, 0.4 NA objective lens with final effective imaging performance of 1.6 NA, which means that it achieves the imaging resolution of 60x objective under the wide FOV of 10x objective.
We report a high-speed FPM technique based on programmable annular illuminations, which achieves the high-speed imaging results of in vitro Hela cells mitosis at a frame rate of 25Hz.
360° 3D reconstruction using a pair of planar mirrors
We propose an adaptive pixel-super-resolved lensfree imaging (APLI) method which can solve the compromise  between lateral resolution and signal-to-noise ratio (SNR).
TIE with annular illumination achieves observation of cell division within 60 hours.
3-D RI of dynamic unstained, live C. elegans worms at a 10.6-Hz volume rate within a volume of 333μm×98μm×21μm.
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