Search Results for author: Hyungjin Chung

Found 23 papers, 8 papers with code

Objective and Interpretable Breast Cosmesis Evaluation with Attention Guided Denoising Diffusion Anomaly Detection Model

no code implementations28 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.

Denoising Image Reconstruction +1

Regularization by Texts for Latent Diffusion Inverse Solvers

no code implementations27 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.

Negation

Prompt-tuning latent diffusion models for inverse problems

no code implementations2 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.

Deblurring Super-Resolution

Generative AI for Medical Imaging: extending the MONAI Framework

2 code implementations27 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.

Anomaly Detection Denoising +2

Score-based Diffusion Models for Bayesian Image Reconstruction

no code implementations25 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.

Image Reconstruction

Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse Problems

1 code implementation10 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.

MRI Reconstruction

Parallel Diffusion Models of Operator and Image for Blind Inverse Problems

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.

Deblurring

Solving 3D Inverse Problems using Pre-trained 2D Diffusion Models

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.

Image Reconstruction

Diffusion Posterior Sampling for General Noisy Inverse Problems

2 code implementations29 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.

Deblurring Retrieval

Progressive Deblurring of Diffusion Models for Coarse-to-Fine Image Synthesis

1 code implementation16 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.

Deblurring Image Generation +1

Improving Diffusion Models for Inverse Problems using Manifold Constraints

2 code implementations2 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.

Colorization Image Inpainting

MR Image Denoising and Super-Resolution Using Regularized Reverse Diffusion

no code implementations23 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.

Image Denoising Super-Resolution

Score-based diffusion models for accelerated MRI

1 code implementation8 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.

Denoising

Unsupervised Deep Learning Methods for Biological Image Reconstruction and Enhancement

no code implementations17 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.

Image Reconstruction Self-Supervised Learning

Simultaneous super-resolution and motion artifact removal in diffusion-weighted MRI using unsupervised deep learning

no code implementations1 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.

Super-Resolution

Feature Disentanglement in generating three-dimensional structure from two-dimensional slice with sliceGAN

no code implementations1 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.

Disentanglement Generative Adversarial Network

Missing Cone Artifacts Removal in ODT using Unsupervised Deep Learning in Projection Domain

no code implementations16 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.

Unpaired Deep Learning for Accelerated MRI using Optimal Transport Driven CycleGAN

no code implementations29 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.

Generative Adversarial Network

Two-Stage Deep Learning for Accelerated 3D Time-of-Flight MRA without Matched Training Data

no code implementations4 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.

Image Reconstruction

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