Search Results for author: Hyungjin Chung

Found 17 papers, 4 papers with code

Improving 3D Imaging with Pre-Trained Perpendicular 2D Diffusion Models

no code implementations15 Mar 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.

Image Generation Image Reconstruction +1

Fast Diffusion Sampler for Inverse Problems by Geometric Decomposition

no code implementations10 Mar 2023 Hyungjin Chung, Suhyeon Lee, Jong Chul Ye

Moreover, our proposed method achieves more than 80 times faster inference time than the previous state-of-the-art method.

MRI Reconstruction

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

no code implementations19 Nov 2022 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

Parallel Diffusion Models of Operator and Image for Blind Inverse Problems

no code implementations19 Nov 2022 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.


Diffusion Posterior Sampling for General Noisy Inverse Problems

1 code implementation29 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.


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

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.


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.


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.

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

Unsupervised Deep Learning for MR Angiography with Flexible Temporal Resolution

no code implementations29 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.

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