33 citations · 132 across the 15 of their papers we have counts for
18 papers · 1 filter
NoiseTransfer: Image Noise Generation with Contrastive Embeddings
Seunghwan Lee, Tae Hyun Kim
Deep image denoising networks have achieved impressive success with the help of a considerably large number of synthetic train datasets. However, real-world denoising is a still ch…
Task Agnostic Restoration of Natural Video Dynamics
Muhammad Kashif Ali, Dongjin Kim, Tae Hyun Kim
In many video restoration/translation tasks, image processing operations are naïvely extended to the video domain by processing each frame independently, disregarding the temporal…
Enriched CNN-Transformer Feature Aggregation Networks for Super-Resolution
Jinsu Yoo, Taehoon Kim, Sihaeng Lee +3
Recent transformer-based super-resolution (SR) methods have achieved promising results against conventional CNN-based methods. However, these approaches suffer from essential short…
Restore from Restored: Single-image Inpainting
Eunhye Lee, Jeongmu Kim, Jisu Kim +1
Recent image inpainting methods have shown promising results due to the power of deep learning, which can explore external information available from the large training dataset. Ho…
Restore from Restored: Single-image Inpainting
Eunhye Lee, Jeongmu Kim, Jisu Kim +1
Recent image inpainting methods show promising results due to the power of deep learning, which can explore external information available from a large training dataset. However, m…
Self-Supervised Adaptation for Video Super-Resolution
Jinsu Yoo, Tae Hyun Kim
Recent single-image super-resolution (SISR) networks, which can adapt their network parameters to specific input images, have shown promising results by exploiting the information…
Deep Motion Blind Video Stabilization
Muhammad Kashif Ali, Sangjoon Yu, Tae Hyun Kim
Despite the advances in the field of generative models in computer vision, video stabilization still lacks a pure regressive deep-learning-based formulation. Deep video stabilizati…
Scene-Adaptive Video Frame Interpolation via Meta-Learning
Myungsub Choi, Janghoon Choi, Sungyong Baik +2
Video frame interpolation is a challenging problem because there are different scenarios for each video depending on the variety of foreground and background motion, frame rate, an…
Restore from Restored: Single Image Denoising with Pseudo Clean Image
Seunghwan Lee, Dongkyu Lee, Donghyeon Cho +2
In this study, we propose a simple and effective fine-tuning algorithm called "restore-from-restored", which can greatly enhance the performance of fully pre-trained image denoisin…
Restore from Restored: Video Restoration with Pseudo Clean Video
Seunghwan Lee, Donghyeon Cho, Jiwon Kim +1
In this study, we propose a self-supervised video denoising method called "restore-from-restored." This method fine-tunes a pre-trained network by using a pseudo clean video during…
Self-Supervised Fast Adaptation for Denoising via Meta-Learning
Seunghwan Lee, Donghyeon Cho, Jiwon Kim +1
Under certain statistical assumptions of noise, recent self-supervised approaches for denoising have been introduced to learn network parameters without true clean images, and thes…
Fast Adaptation to Super-Resolution Networks via Meta-Learning
Seobin Park, Jinsu Yoo, Donghyeon Cho +2
Conventional supervised super-resolution (SR) approaches are trained with massive external SR datasets but fail to exploit desirable properties of the given test image. On the othe…
Fast and Full-Resolution Light Field Deblurring using a Deep Neural Network
Jonathan Samuel Lumentut, Tae Hyun Kim, Ravi Ramamoorthi +1
Restoring a sharp light field image from its blurry input has become essential due to the increasing popularity of parallax-based image processing. State-of-the-art blind light fie…
Online Video Deblurring via Dynamic Temporal Blending Network
Tae Hyun Kim, Kyoung Mu Lee, Bernhard Schölkopf +1
State-of-the-art video deblurring methods are capable of removing non-uniform blur caused by unwanted camera shake and/or object motion in dynamic scenes. However, most existing me…
Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring
Seungjun Nah, Tae Hyun Kim, Kyoung Mu Lee
Non-uniform blind deblurring for general dynamic scenes is a challenging computer vision problem as blurs arise not only from multiple object motions but also from camera shake, sc…
Occlusion-Aware Video Deblurring with a New Layered Blur Model
Byeongjoo Ahn, Tae Hyun Kim, Wonsik Kim +1
We present a deblurring method for scenes with occluding objects using a carefully designed layered blur model. Layered blur model is frequently used in the motion deblurring probl…
Dynamic Scene Deblurring using a Locally Adaptive Linear Blur Model
Tae Hyun Kim, Seungjun Nah, Kyoung Mu Lee
State-of-the-art video deblurring methods cannot handle blurry videos recorded in dynamic scenes, since they are built under a strong assumption that the captured scenes are static…
Generalized Video Deblurring for Dynamic Scenes
Tae Hyun Kim, Kyoung Mu Lee
Several state-of-the-art video deblurring methods are based on a strong assumption that the captured scenes are static. These methods fail to deblur blurry videos in dynamic scenes…