Image/Video Acquisition

Handling Real-World Data

While synthetically generated datasets have been widely used for training neural networks for image restoration, such networks do not perform well on real-world images due to the significant domain gap between naively synthesized datasets and real-world degradations. To better handle real-world degraded images, we have explored different possibilities such as real-world datasets and realistic degradation synthesis. Our RealBlur dataset, which is a dataset consisting of real-world blurred images and their ground-truth sharp images, is now utilized as a standard benchmark dataset by numerous works.

• ParamISP: Learned Forward and Inverse ISPs using Camera Parameters

  • Woohyeok Kim, Geonu Kim, Junyong Lee, Seungyong Lee, Seung-Hwan Baek, Sunghyun Cho

  • CVPR 2024 [Project]

• Realistic Blur Synthesis for Learning Image Deblurring

  • Jaesung Rim, Geonung Kim, Jungeon Kim, Junyong Lee, Seungyong Lee, Sunghyun Cho

  • ECCV 2022 [Project]

  • RSBlur dataset [Benchmarks]

• Real-World Blur Dataset for Learning and Benchmarking Deblurring Algorithms

  • Jaesung Rim, Haeyun Lee, Jucheol Won, Sunghyun Cho

  • ECCV 2020 [Project]

  • RealBlur dataset [Benchmarks]

Priors and Neural Network Frameworks for Image Restoration

• DC-VSR: Spatially and Temporally Consistent Video Super-Resolution with Video Diffusion Prior

  • Janghyeok Han, Gyujin Sim, Geonung Kim, Hyun-Seung Lee, Kyuha Choi, Youngseok Han, Sunghyun Cho

  • SIGGRAPH 2025 [Project]

• UGPNet: Universal Generative Prior for Image Restoration

  • Hwayoon Lee, Kyoungkook Kang, Hyeongmin Lee, Seung-Hwan Baek, Sunghyun Cho

  • WACV 2024 [Project]

• BigColor: Colorization using a Generative Color Prior for Natural Images

  • Geonung Kim, Kyoungkook Kang, Seongtae Kim, Hwayoon Lee, Sehoon Kim, Jonghyun Kim, Seung-Hwan Baek, Sunghyun Cho

  • ECCV 2022 [Project] [Code]

• Single Image Defocus Deblurring Using Kernel-Sharing Parallel Atrous Convolutions

  • Hyeongseok Son, Junyong Lee, Sunghyun Cho, Seungyong Lee

  • ICCV 2021 [ArXiv] [Code]

• Iterative Filter Adaptive Network for Single Image Defocus Deblurring

  • Junyong Lee, Hyeongseok Son, Jaesung Rim, Sunghyun Cho, Seungyong Lee

  • CVPR 2021 [PDF] [Supp] [Presentation Video/Poster/Slides] [Code & Data]

  • RealDOF dataset [Benchmark]

• Locally Adaptive Channel Attention-based Network for Denoising Images

  • Haeyun Lee, Sunghyun Cho

  • IEEE Access, 2020 [PROJECT]

Camera ISPs

Camera ISPs produce visually-pleasing images from RAW data captured by camera sensors. Specifically, camera ISPs perform two tasks: image restoration and enhancement. The goals of our research are twofold. 1) We aim at accurate modeling of real-world camera ISPs so that we can more accurately model real-world image degradations. 2) We aim at replacing traditional camera ISPs with learnable ISPs for higher-quality imaging.

• ParamISP: Learned Forward and Inverse ISPs using Camera Parameters

  • Woohyeok Kim, Geonu Kim, Junyong Lee, Seungyong Lee, Seung-Hwan Baek, Sunghyun Cho

  • CVPR 2024 [Project]

• CLIPtone: Unsupervised Learning for Text-based Image Tone Adjustment

  • Hyeongmin Lee, Kyoungkook Kang, Jungseul Ok, Sunghyun Cho

  • CVPR 2024 [Project]

Beyond Single Images

Traditional image restoration tasks mainly focus on restoring a single image that is already captured by using a single camera. On the other hand, recent smartphones provide more than one cameras and additional sensors such as gyro sensors. Moreover, we may also program the way to capture images such as modulating the exposure time. We seek to exploit such possibilities.

• Gyro-based Neural Single Image Deblurring

  • Heemin Yang, Jaesung Rim, Seungyong Lee, Seung-Hwan Baek, Sunghyun Cho

  • CVPR 2025 [Project]

• Deep Hybrid Camera Deblurring for Smartphone Cameras

  • Jaesung Rim, Junyong Lee, Heemin Yang, Sunghyun Cho

  • SIGGRAPH 2024 [Project]

  • HCBlur dataset [Project]

• Burst Image Super-Resolution with Base Frame Selection

  • Sanghyun Kim, Min Jung Lee, Woohyeok Kim, Deunsol Jung, Jaesung Rim, Sunghyun Cho, Minsu Cho

  • NTIRE 2024 (CVPR 2024 Workshop)

• Reference-based Video Super-Resolution Using Multi-Camera Video Triplets

  • Junyong Lee, Myeonghee Lee, Sunghyun Cho, Seungyong Lee

  • CVPR 2022 [Project]

Visual Recognition from Low-Quality Images

While many recent visual recognition algorithms perform highly accurately on high-quality images, their performance severely degrade on real-world low-quality images captured in extreme environments. We also work on improving visual recognition performance on such low-quality images.

• Human Pose Estimation in Extremely Low-Light Conditions

  • Sohyun Lee, Jaesung Rim, Boseung Jeong, Geonu Kim, Byungju Woo, Haechan Lee, Sunghyun Cho, Suha Kwak

  • CVPR 2023 [Project]

  • ExLPose dataset [Project] [Benchmark]

• URIE: Universal Image Enhancement for Visual Recognition in the Wild

  • Taeyoung Son, Juwon Kang, Namyup Kim, Sunghyun Cho, Suha Kwak

  • ECCV 2020 [ArXiv]

Videos

• DC-VSR: Spatially and Temporally Consistent Video Super-Resolution with Video Diffusion Prior

  • Janghyeok Han, Gyujin Sim, Geonung Kim, Hyun-Seung Lee, Kyuha Choi, Youngseok Han, Sunghyun Cho

  • SIGGRAPH 2025 [Project]

• Real-Time Video Deblurring via Lightweight Motion Compensation

  • Hyeongseok Son, Junyong Lee, Sunghyun Cho, Seungyong Lee

  • Computer Graphics Forum (special issue on Pacific Graphics 2022), 2022 [Project]

• Reference-based Video Super-Resolution Using Multi-Camera Video Triplets

  • Junyong Lee, Myeonghee Lee, Sunghyun Cho, Seungyong Lee

  • CVPR 2022 [Project]

  • RealMCVSR dataset [Project] [Benchmark]

• Recurrent Video Deblurring with Blur-Invariant Motion Estimation and Pixel Volumes

  • Hyeongseok Son, Junyong Lee, Jonghyeop Lee, Sunghyun Cho, Seungyong Lee

  • ACM Transactions on Graphics (TOG), vol. 40, no. 5, 2021 [PDF] [Code]

• Video Upright Adjustment and Stabilization

  • Jucheol Won, Sunghyun Cho

  • BMVC 2019 [PROJECT]

• Video Deblurring for Hand-held Cameras Using Patch-based Synthesis

  • Sunghyun Cho, Jue Wang, Seungyong Lee

  • ACM Transactions on Graphics (SIGGRAPH 2012) vol. 31, no. 4, article no. 64, July 2012 [PROJECT]

Past Projects

I have also worked on many other projects on low-level vision that are not based on deep learning. Some of them are listed below.

• Convergence Analysis of MAP based Blur Kernel Estimation

  • Sunghyun Cho, Seungyong Lee

  • ICCV 2017 [PDF] [Supplementary]

• Good Image Priors for Non-blind Deconvolution: Generic vs Specific

  • Libin Sun, Sunghyun Cho, Jue Wang, James Hays

  • ECCV 2014 [PROJECT]

• Deblurring Low-light Images with Light Streaks

  • Zhe Hu, Sunghyun Cho, Jue Wang, Ming-Hsuan Yang

  • CVPR 2014 [PROJECT]

• Edge-based Blur Kernel Estimation Using Patch Priors

  • Libin Sun, Sunghyun Cho, Jue Wang, James Hays

  • ICCP 2013 [PROJECT]

• Handling Outliers in Non-blind Image Deconvolution

  • Sunghyun Cho, Jue Wang, Seungyong Lee

  • ICCV 2011 (oral presentation, acceptance ratio: 3.1%) [PDF] [CODE]

• Fast Motion Deblurring

  • Sunghyun Cho, Seungyong Lee

  • ACM Transactions on Graphics (SIGGRAPH ASIA 2009)

  • vol. 28, no. 5, article no. 145, Dec. 2009 [PROJECT]

Tech Transfer

Since my Ph.D. study, I’ve been trying to make image deblurring more practical solution for consumer photography. Specifically, I’ve worked on blind deconvolution, non-blind deconvolution, noise & outlier handling, and non-uniform deblurring. Especially, my efficient blind deconvolution method, which was presented at SIGGRAPH Asia 2009, has been proven to be one of the fastest and most reliable methods by several following works by other researchers.

When I was at Adobe Research, I worked on a tech transfer project, which aimed at shipping deblurring technology with Adobe Photoshop. The first version of “Shake Reduction”, which was based on my research, was first released in the summer of 2013 as a new feature of Photoshop CC.

Image/Video Generation & Manipulation

Real-World Image/Video Editing

• Addressing Attribute Leakage in Text Embeddings for Image Editing with Diffusion Models

  • Sunung Mun, Jinhwan Nam, Sunghyun Cho, Jungseul Ok

  • ICCV 2025 [Project]

• RNA:Video Editing with ROI-based Neural Atlas

  • Jaekyeong Lee, Geonung Kim, Sunghyun Cho

  • ACCV 2024 [Project]

• CLIPtone: Unsupervised Learning for Text-based Image Tone Adjustment

  • Hyeongmin Lee, Kyoungkook Kang, Jungseul Ok, Sunghyun Cho

  • CVPR 2024 [Project]

• GAN Inversion for Out-of-Range Images with Geometric Transformations

  • Kyoungkook Kang, Seongtae Kim, Sunghyun Cho

  • ICCV 2021 [PROJECT] [PDF] [Supp]

• Deep Color Transfer using Histogram Analogy

  • Junyong Lee, Hyeongseok Son, Gunhee Lee, Jonghyeop Lee, Sunghyun Cho, Seungyong Lee

  • The Visual Computer Journal (CGI 2020)

  • [Paper][Supplementary][Code & Data]

• Naturalness-Preserving Image Tone Enhancement Using Generative Adversarial Networks

  • Hyeongseok Son, Gunhee Lee, Sunghyun Cho, Seungyong Lee

  • Computer Graphics Forum (special issue on Pacific Graphics 2019), 2019 [Paper]

  • Best Paper Award

Image/Video Synthesis

• VideoFrom3D: 3D Scene Video Generation via Complementary Image and Video Diffusion Models

  • Geonung Kim, Janghyeok Han, Sunghyun Cho

  • SIGGRAPH Asia 2025 [Project]

• FloVD: Optical Flow Meets Video Diffusion Model for Enhanced Camera-Controlled Video Synthesis

  • Wonjoon Jin, Qi Dai, Chong Luo, Seung-Hwan Baek, Sunghyun Cho

  • CVPR 2025 [Project]

• Diffusion Model Compression for Image-to-Image Translation

  • Geonung Kim, Beomsu Kim, Eunhyeok Park, Sunghyun Cho

  • ACCV 2024 [Project]

• DynaGAN: Dynamic Few-shot Adaptation of GANs to Multiple Domains

  • Seongtae Kim, Kyoungkook Kang, Geonung Kim, Seung-Hwan Baek, Sunghyun Cho

  • SIGGRAPH Asia 2022 (Conference Track) [Project]

• Dr.3D: Adapting 3D GANs to Artistic Drawings

  • Wonjoon Jin, Nuri Ryu, Geonung Kim, Seung-Hwan Baek, Sunghyun Cho

  • SIGGRAPH Asia 2022 (Conference Track) [Project]

3D Acquisition, Generation and Manipulation

3D Acquisition

• Leveraging Learned Image Prior for 3D Gaussian Compression

  • Seungjoo Shin, Jaesik Park, Sunghyun Cho

  • ICCV 2025 Workshop (ECLR)

• Exploiting Deblurring Networks for Radiance Fields

  • Haeyun Choi, Heemin Yang, Janghyeok Han, Sunghyun Cho

  • CVPR 2025 [Project]

• Locality-aware Gaussian Compression for Fast and High-quality Rendering

  • Seungjoo Shin, Jaesik Park, Sunghyun Cho

  • ICLR 2025 [Project]

• Generalizable Novel-View Synthesis using a Stereo Camera

  • Haechan Lee, Wonjoon Jin, Seung-Hwan Baek, Sunghyun Cho

  • CVPR 2024 [Project]

  • StereoNVS dataset [Project]

• Neural Spectro-polarimetric Fields

  • Youngchan Kim, Wonjoon Jin, Sunghyun Cho, Seung-Hwan Baek

  • SIGGRAPH Asia 2023 [Project]

• ExBluRF: Efficient Radiance Fields for Extreme Motion Blurred Images

  • Dongwoo Lee, Jeongtaek Oh, Jaesung Rim, Sunghyun Cho, Kyoung Mu Lee

  • ICCV 2023 [Paper]

  • Exblur dataset [Project]

• CTRL-C: Camera calibration TRansformer with Line-Classification

  • Jinwoo Lee, Hyunsung Go, Hyunjoon Lee, Sunghyun Cho, Minhyuk Sung, Junho Kim

  • ICCV 2021 [ArXiv] [Code]

3D Generation & Manipulation

• Elevating 3D Models: High-Quality Texture and Geometry Refinement from a Low-Quality Model

  • Nuri Ryu, Jiyun Won, Jooeun Son, Minsu Gong, Joo-Haeng Lee, Sunghyun Cho

  • SIGGRAPH 2025 [Project]

• 360° Reconstruction From a Single Image Using Space Carved Outpainting

  • Nuri Ryu, Minsu Gong, Geonung Kim, Joo-Haeng Lee, Sunghyun Cho

  • SIGGRAPH Asia 2023 [Project]

• 3D-Aware Generative Model for Improved Side-View Image Synthesis

  • Kyungmin Jo, Wonjoon Jin, Jaegul Choo, Hyunjoon Lee, Sunghyun Cho

  • ICCV 2023 [Project]

• Exp-GAN: 3D-Aware Facial Image Generation with Expression Control

  • Yeonkyeong Lee, Taeho Choi, Hyunsung Go, Hyunjoon Lee, Sunghyun Cho, Junho Kim

  • ACCV 2022 (Oral presentation) [Paper]

• Dr.3D: Adapting 3D GANs to Artistic Drawings

  • Wonjoon Jin, Nuri Ryu, Geonung Kim, Seung-Hwan Baek, Sunghyun Cho

  • SIGGRAPH Asia 2022 (Conference Track) [Project]

• 3D Scene Painting via Semantic Image Synthesis

  • Jaebong Jeong, Janghun Jo, Sunghyun Cho, Jaesik Park

  • CVPR 2022 [Project]