no code implementations • 28 Feb 2024 • Sangjoon Park, Yong Bae Kim, Jee Suk Chang, Seo Hee Choi, Hyungjin Chung, Ik Jae Lee, Hwa Kyung Byun
As advancements in the field of breast cancer treatment continue to progress, the assessment of post-surgical cosmetic outcomes has gained increasing significance due to its substantial impact on patients' quality of life.
no code implementations • 27 Nov 2023 • Jeongsol Kim, Geon Yeong Park, Hyungjin Chung, Jong Chul Ye
The recent advent of diffusion models has led to significant progress in solving inverse problems, leveraging these models as effective generative priors.
no code implementations • 2 Oct 2023 • Hyungjin Chung, Jong Chul Ye, Peyman Milanfar, Mauricio Delbracio
We propose a new method for solving imaging inverse problems using text-to-image latent diffusion models as general priors.
no code implementations • 28 Aug 2023 • Riccardo Barbano, Alexander Denker, Hyungjin Chung, Tae Hoon Roh, Simon Arrdige, Peter Maass, Bangti Jin, Jong Chul Ye
Denoising diffusion models have emerged as the go-to framework for solving inverse problems in imaging.
2 code implementations • 27 Jul 2023 • Walter H. L. Pinaya, Mark S. Graham, Eric Kerfoot, Petru-Daniel Tudosiu, Jessica Dafflon, Virginia Fernandez, Pedro Sanchez, Julia Wolleb, Pedro F. da Costa, Ashay Patel, Hyungjin Chung, Can Zhao, Wei Peng, Zelong Liu, Xueyan Mei, Oeslle Lucena, Jong Chul Ye, Sotirios A. Tsaftaris, Prerna Dogra, Andrew Feng, Marc Modat, Parashkev Nachev, Sebastien Ourselin, M. Jorge Cardoso
We have implemented these models in a generalisable fashion, illustrating that their results can be extended to 2D or 3D scenarios, including medical images with different modalities (like CT, MRI, and X-Ray data) and from different anatomical areas.
no code implementations • 25 May 2023 • Michael T. McCann, Hyungjin Chung, Jong Chul Ye, Marc L. Klasky
This paper explores the use of score-based diffusion models for Bayesian image reconstruction.
1 code implementation • ICCV 2023 • Suhyeon Lee, Hyungjin Chung, Minyoung Park, Jonghyuk Park, Wi-Sun Ryu, Jong Chul Ye
Diffusion models have become a popular approach for image generation and reconstruction due to their numerous advantages.
1 code implementation • 10 Mar 2023 • Hyungjin Chung, Suhyeon Lee, Jong Chul Ye
In this study, we propose a novel and efficient diffusion sampling strategy that synergistically combines the diffusion sampling and Krylov subspace methods.
1 code implementation • CVPR 2023 • Hyungjin Chung, Dohoon Ryu, Michael T. McCann, Marc L. Klasky, Jong Chul Ye
Diffusion models have emerged as the new state-of-the-art generative model with high quality samples, with intriguing properties such as mode coverage and high flexibility.
no code implementations • CVPR 2023 • Hyungjin Chung, Jeongsol Kim, Sehui Kim, Jong Chul Ye
We show the efficacy of our method on two representative tasks -- blind deblurring, and imaging through turbulence -- and show that our method yields state-of-the-art performance, while also being flexible to be applicable to general blind inverse problems when we know the functional forms.
2 code implementations • 29 Sep 2022 • Hyungjin Chung, Jeongsol Kim, Michael T. McCann, Marc L. Klasky, Jong Chul Ye
Diffusion models have been recently studied as powerful generative inverse problem solvers, owing to their high quality reconstructions and the ease of combining existing iterative solvers.
1 code implementation • 16 Jul 2022 • Sangyun Lee, Hyungjin Chung, Jaehyeon Kim, Jong Chul Ye
We further propose a blur diffusion as a special case, where each frequency component of an image is diffused at different speeds.
2 code implementations • 2 Jun 2022 • Hyungjin Chung, Byeongsu Sim, Dohoon Ryu, Jong Chul Ye
Recently, diffusion models have been used to solve various inverse problems in an unsupervised manner with appropriate modifications to the sampling process.
no code implementations • 23 Mar 2022 • Hyungjin Chung, Eun Sun Lee, Jong Chul Ye
Our network, trained only with coronal knee scans, excels even on out-of-distribution in vivo liver MRI data, contaminated with complex mixture of noise.
no code implementations • CVPR 2022 • Hyungjin Chung, Byeongsu Sim, Jong Chul Ye
In this work, we show that starting from Gaussian noise is unnecessary.
1 code implementation • 8 Oct 2021 • Hyungjin Chung, Jong Chul Ye
Leveraging the learned score function as a prior, here we introduce a way to sample data from a conditional distribution given the measurements, such that the model can be readily used for solving inverse problems in imaging, especially for accelerated MRI.
no code implementations • 17 May 2021 • Mehmet Akçakaya, Burhaneddin Yaman, Hyungjin Chung, Jong Chul Ye
Recently, deep learning approaches have become the main research frontier for biological image reconstruction and enhancement problems thanks to their high performance, along with their ultra-fast inference times.
no code implementations • 1 May 2021 • Hyungjin Chung, Jaehyun Kim, Jeong Hee Yoon, Jeong Min Lee, Jong Chul Ye
To the best of our knowledge, the proposed method is the first to tackle super-resolution and motion artifact correction simultaneously in the context of MRI using unsupervised learning.
no code implementations • 1 May 2021 • Hyungjin Chung, Jong Chul Ye
Hence, we combine sliceGAN with AdaIN to endow the model with the ability to disentangle the features and control the synthesis.
no code implementations • 16 Mar 2021 • Hyungjin Chung, Jaeyoung Huh, Geon Kim, Yong Keun Park, Jong Chul Ye
Optical diffraction tomography (ODT) produces three dimensional distribution of refractive index (RI) by measuring scattering fields at various angles.
no code implementations • 29 Aug 2020 • Gyutaek Oh, Byeongsu Sim, Hyungjin Chung, Leonard Sunwoo, Jong Chul Ye
Recently, deep learning approaches for accelerated MRI have been extensively studied thanks to their high performance reconstruction in spite of significantly reduced runtime complexity.
no code implementations • 4 Aug 2020 • Hyungjin Chung, Eunju Cha, Leonard Sunwoo, Jong Chul Ye
Time-of-flight magnetic resonance angiography (TOF-MRA) is one of the most widely used non-contrast MR imaging methods to visualize blood vessels, but due to the 3-D volume acquisition highly accelerated acquisition is necessary.
no code implementations • 29 Mar 2020 • Eunju Cha, Hyungjin Chung, Eung Yeop Kim, Jong Chul Ye
This is because high spatio-temporal resolution ground-truth images are not available for tMRA.