33 citations · 132 across the 15 of their papers we have counts for
6 papers · 1 filter
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…