181 citations · 314 across the 9 of their papers we have counts for
9 papers
RawGen: Learning Camera Raw Image Generation
Dongyoung Kim, Junyong Lee, Abhijith Punnappurath +4
Cameras capture scene-referred linear raw images, which are processed by onboard image signal processors (ISPs) into display-referred 8-bit sRGB outputs. Although raw data is more…
RAW-Domain Degradation Models for Realistic Smartphone Super-Resolution
Ali Mosleh, Faraz Ali, Fengjia Zhang +4
Digital zoom on smartphones relies on learning-based super-resolution (SR) models that operate on RAW sensor images, but obtaining sensor-specific training data is challenging due…
Deep Hybrid Camera Deblurring for Smartphone Cameras
Jaesung Rim, Junyong Lee, Heemin Yang +1
Mobile cameras, despite their significant advancements, still have difficulty in low-light imaging due to compact sensors and lenses, leading to longer exposures and motion blur. T…
ParamISP: Learned Forward and Inverse ISPs using Camera Parameters
Woohyeok Kim, Geonu Kim, Junyong Lee +3
RAW images are rarely shared mainly due to its excessive data size compared to their sRGB counterparts obtained by camera ISPs. Learning the forward and inverse processes of camera…
Real-Time Video Deblurring via Lightweight Motion Compensation
Hyeongseok Son, Junyong Lee, Sunghyun Cho +1
While motion compensation greatly improves video deblurring quality, separately performing motion compensation and video deblurring demands huge computational overhead. This paper…
Reference-based Video Super-Resolution Using Multi-Camera Video Triplets
Junyong Lee, Myeonghee Lee, Sunghyun Cho +1
We propose the first reference-based video super-resolution (RefVSR) approach that utilizes reference videos for high-fidelity results. We focus on RefVSR in a triple-camera settin…
Recurrent Video Deblurring with Blur-Invariant Motion Estimation and Pixel Volumes
Hyeongseok Son, Junyong Lee, Jonghyeop Lee +2
For the success of video deblurring, it is essential to utilize information from neighboring frames. Most state-of-the-art video deblurring methods adopt motion compensation betwee…
Single Image Defocus Deblurring Using Kernel-Sharing Parallel Atrous Convolutions
Hyeongseok Son, Junyong Lee, Sunghyun Cho +1
This paper proposes a novel deep learning approach for single image defocus deblurring based on inverse kernels. In a defocused image, the blur shapes are similar among pixels alth…
Iterative Filter Adaptive Network for Single Image Defocus Deblurring
Junyong Lee, Hyeongseok Son, Jaesung Rim +2
We propose a novel end-to-end learning-based approach for single image defocus deblurring. The proposed approach is equipped with a novel Iterative Filter Adaptive Network (IFAN) t…