Search Results for author: Yilin Luo

Found 6 papers, 1 papers with code

Neural network-based image reconstruction in swept-source optical coherence tomography using undersampled spectral data

no code implementations4 Mar 2021 Yijie Zhang, Tairan Liu, Manmohan Singh, Yilin Luo, Yair Rivenson, Kirill V. Larin, Aydogan Ozcan

Using 2-fold undersampled spectral data (i. e., 640 spectral points per A-line), the trained neural network can blindly reconstruct 512 A-lines in ~6. 73 ms using a desktop computer, removing spatial aliasing artifacts due to spectral undersampling, also presenting a very good match to the images of the same samples, reconstructed using the full spectral OCT data (i. e., 1280 spectral points per A-line).

Image Reconstruction

Deep learning-based virtual refocusing of images using an engineered point-spread function

no code implementations22 Dec 2020 Xilin Yang, Luzhe Huang, Yilin Luo, Yichen Wu, Hongda Wang, Yair Rivenson, Aydogan Ozcan

We present a virtual image refocusing method over an extended depth of field (DOF) enabled by cascaded neural networks and a double-helix point-spread function (DH-PSF).

Image Reconstruction

Recurrent neural network-based volumetric fluorescence microscopy

no code implementations21 Oct 2020 Luzhe Huang, Yilin Luo, Yair Rivenson, Aydogan Ozcan

Volumetric imaging of samples using fluorescence microscopy plays an important role in various fields including physical, medical and life sciences.

Image Reconstruction

Single-shot autofocusing of microscopy images using deep learning

no code implementations21 Mar 2020 Yilin Luo, Luzhe Huang, Yair Rivenson, Aydogan Ozcan

We demonstrate a deep learning-based offline autofocusing method, termed Deep-R, that is trained to rapidly and blindly autofocus a single-shot microscopy image of a specimen that is acquired at an arbitrary out-of-focus plane.

Three-dimensional virtual refocusing of fluorescence microscopy images using deep learning

1 code implementation31 Jan 2019 Yichen Wu, Yair Rivenson, Hongda Wang, Yilin Luo, Eyal Ben-David, Laurent A. Bentolila, Christian Pritz, Aydogan Ozcan

Three-dimensional (3D) fluorescence microscopy in general requires axial scanning to capture images of a sample at different planes.

Cross-modality deep learning brings bright-field microscopy contrast to holography

no code implementations17 Nov 2018 Yichen Wu, Yilin Luo, Gunvant Chaudhari, Yair Rivenson, Ayfer Calis, Kevin De Haan, Aydogan Ozcan

Deep learning brings bright-field microscopy contrast to holographic images of a sample volume, bridging the volumetric imaging capability of holography with the speckle- and artifact-free image contrast of bright-field incoherent microscopy.

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