6 papers · 1 filter
sRGB Real Noise Modeling via Noise-Aware Sampling with Normalizing Flows
Dongjin Kim, Donggoo Jung, Sungyong Baik +1
Noise poses a widespread challenge in signal processing, particularly when it comes to denoising images. Although convolutional neural networks (CNNs) have exhibited remarkable suc…
YeTI: You Only Need Two Noisy Images for Real-World sRGB Noise Generation
Jaekyun Ko, Byung Wan Lim, Soomin Lee +2
Real-world sRGB image denoising remains challenging due to the nonlinear characteristics of sensor noise and the difficulty of acquiring aligned clean-noisy image pairs. Supervised…
Continuous Degradation Modeling via Latent Flow Matching for Real-World Super-Resolution
Hyeonjae Kim, Dongjin Kim, Eugene Jin +1
While deep learning-based super-resolution (SR) methods have shown impressive outcomes with synthetic degradation scenarios such as bicubic downsampling, they frequently struggle t…
IDF: Iterative Dynamic Filtering Networks for Generalizable Image Denoising
Dongjin Kim, Jaekyun Ko, Muhammad Kashif Ali +1
Image denoising is a fundamental challenge in computer vision, with applications in photography and medical imaging. While deep learning-based methods have shown remarkable success…
Harnessing Meta-Learning for Controllable Full-Frame Video Stabilization
Muhammad Kashif Ali, Eun Woo Im, Dongjin Kim +4
Video stabilization remains a fundamental problem in computer vision, particularly pixel-level synthesis solutions for video stabilization, which synthesize full-frame outputs, add…
Harnessing Meta-Learning for Improving Full-Frame Video Stabilization
Muhammad Kashif Ali, Eun Woo Im, Dongjin Kim +1
Video stabilization is a longstanding computer vision problem, particularly pixel-level synthesis solutions for video stabilization which synthesize full frames add to the complexi…