2 citations · 2 across the 1 of their papers we have counts for
5 papers
DeepRegularizer: Rapid Resolution Enhancement of Tomographic Imaging using Deep Learning
DongHun Ryu, Dongmin Ryu, YoonSeok Baek +8
Optical diffraction tomography measures the three-dimensional refractive index map of a specimen and visualizes biochemical phenomena at the nanoscale in a non-destructive manner.…
Calibration-free quantitative phase imaging using data-driven aberration modeling
Taean Chang, Youngju Jo, Gunho Choi +3
We present a data-driven approach to compensate for optical aberration in calibration-free quantitative phase imaging (QPI). Unlike existing methods that require additional measure…
Deep learning-enabled image quality control in tomographic reconstruction: Robust optical diffraction tomography
Donghun Ryu, Youngju Jo, Jihyeong Yoo +6
In tomographic reconstruction, the image quality of the reconstructed images can be significantly degraded by defects in the measured two-dimensional (2D) raw image data. Despite t…
Deep learning approach to coherent noise reduction in optical diffraction tomography
Gunho Choi, Donghun Ryu, Youngju Jo +4
We present a deep neural network to reduce coherent noise in three-dimensional quantitative phase imaging. Inspired by the cycle generative adversarial network, the denoising netwo…
Quantitative Phase Imaging and Artificial Intelligence: A Review
YoungJu Jo, Hyungjoo Cho, Sang Yun Lee +4
Recent advances in quantitative phase imaging (QPI) and artificial intelligence (AI) have opened up the possibility of an exciting frontier. The fast and label-free nature of QPI e…