Laboratory of Advanced Imaging Technology (LAIT)

컴퓨터 비전 및 바이오 영상신호처리 연구실

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컴퓨터 비전 및 바이오 영상신호처리 연구실

컴퓨터 비전 및 바이오 영상신호처리 연구실에서는 사람과 영상 사이의 다양한 상호작용을 보다 더 쉽고, 정확하고, 직관적으로 만들기 위한 모델을 연구합니다. 이를 위해 새로운 기계학습 기법을 개발하되, 단순히 기능을 하는 모델을 넘어서서 신호처리 기반의 분석을 바탕으로 효율적이고, 성능이 좋으면서도, 동작 원리를 잘 이해할 수 있는 모델을 만드는 것을 목표로 합니다.

Our main research area lies at the intersection of computer vision, machine learning, and inverse problems, including natural image recovery and medical imaging. We have a strong interest in generative models, representation learning, and the use of signal processing theories for image processing. Our goal is to build strong and intelligent signal processing models, capable of recreating the world we perceive. We aim to establish a bridge between signal processing and deep learning, taking the best of both worlds. We study deep learning models that learn structural priors by synthesizing and modeling millions of images and videos. Along the way, the learned models provide insights to seek mathematical elegance and a clear understanding of our world, which in turn encourages us to find better models to analyze signals in nature.


computer vision, machine learning, inverse problems, generative models, medical imaging


generative models, medical imaging, self-supervision, representation learning, interpretable models

Research Keywords and Topics

Inverse problems for various imaging modalities:
- natural image restorations (super-resolution, denoising, deblurring, etc.)
- medical image reconstructions (MRI, CT, SIM, Cryo-EM, DOT, EEG, fMRI, etc.)

Bridging between signal processing and deep learning communities:
- providing a design principle for deep learning architectures
- network analysis using topological data analysis (TDA)

Deep generative models:
- developing a high fidelity and diverse image-to-image translation model
- improving generative models based on theoretical understandings

Research Publications

•TMI / Time-Dependent Deep Image Prior / J. Yoo, K.H. Jin, H. Gupta, J. Yerly, M. Stuber, M. Unser / 2021
•ICML / Reliable Fidelity and Diversity Metrics for Generative Models / M.F. Naeem, S.J. Oh, Y.J. Choi, Y.J. Uh, J.Yoo / 2020
•CVPR / StarGAN v2: Diverse Image Synthesis for Multiple Domains / Y.J. Choi, Y.J. Uh, J.Yoo, J.W. Ha / 2020
•CVPR / Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Analysis and a New Strategy / J. Yoo, N.H. Ahn, K.A. Sohn / 2020
•ICCV / Photorealistic Style Transfer via Wavelet Transforms / J.Yoo, S.H. Chun, Y.J. Uh, B. Kang, J.W. Ha / 2019
•ICLR / Large-Scale Answerer in Questioner’s Mind for Visual Dialog Question Generation/ S.W. Lee, T. Gao, S. Yang, J. Yoo, J.W. Ha / 2019


  • EE. 정보/통신
  • EE01. 정보이론
  • EE0108. 인공지능


  • 정보-지식-지능화 사회 구현
  • 012300. 인공지능/지능로봇 기술


  • 녹색기술관련 과제 아님
  • 녹색기술관련 과제 아님
  • 999. 녹색기술 관련과제 아님


  • IT 분야
  • 정보처리 시스템 및 S/W
  • 010314. 신호처리기술(영상/음성처리/인식/합성)