no code implementations • 28 Mar 2023 • Minsoo Kang, Hyewon Yoo, Eunhee Kang, Sehwan Ki, Hyong-Euk Lee, Bohyung Han
We propose an information-theoretic knowledge distillation approach for the compression of generative adversarial networks, which aims to maximize the mutual information between teacher and student networks via a variational optimization based on an energy-based model.
no code implementations • ICCV 2023 • Anqi Yang, Eunhee Kang, Hyong-Euk Lee, Aswin C. Sankaranarayanan
Diffractive blur and low light levels are two fundamental challenges in producing high-quality photographs in under-display cameras (UDCs).
no code implementations • CVPR 2021 • Kinam Kwon, Eunhee Kang, Sangwon Lee, Su-Jin Lee, Hyong-Euk Lee, ByungIn Yoo, Jae-Joon Han
However, this causes inevitable image degradation in the form of spatially variant blur and noise because of the opaque display in front of the camera.
1 code implementation • 26 Jun 2018 • Eunhee Kang, Hyun Jung Koo, Dong Hyun Yang, Joon Bum Seo, Jong Chul Ye
Although this reduces the total radiation dose, the image quality during the low-dose phases is significantly degraded.
1 code implementation • 31 Jul 2017 • Eunhee Kang, Jaejun Yoo, Jong Chul Ye
To address this problem, we recently proposed a deep convolutional neural network (CNN) for low-dose X-ray CT and won the second place in 2016 AAPM Low-Dose CT Grand Challenge.
2 code implementations • 4 Mar 2017 • Eunhee Kang, Junhong Min, Jong Chul Ye
Model based iterative reconstruction (MBIR) algorithms for low-dose X-ray CT are computationally complex because of the repeated use of the forward and backward projection.
no code implementations • 31 Oct 2016 • Eunhee Kang, Junhong Min, Jong Chul Ye
To the best of our knowledge, this work is the first deep learning architecture for low-dose CT reconstruction that has been rigorously evaluated and proven for its efficacy.