9 papers · 1 filter
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…
PRISM: Latent Composition Consistency for Single-Image Reflection Removal
Junseong Shin, Tae Hyun Kim
Single-image reflection removal (SIRR) seeks to recover the transmission layer from a mixture corrupted by reflections -- a severely ill-posed problem. Existing methods operate in…
UCMNet: Uncertainty-Aware Context Memory Network for Under-Display Camera Image Restoration
Daehyun Kim, Youngmin Kim, Yoon Ju Oh +1
Under-display cameras (UDCs) allow for full-screen designs by positioning the imaging sensor underneath the display. Nonetheless, light diffraction and scattering through the vario…
Diffusion-Based sRGB Real Noise Generation via Prompt-Driven Noise Representation Learning
Jaekyun Ko, Dongjin Kim, Soomin Lee +2
Denoising in the sRGB image space is challenging due to large noise variability. Although end-to-end methods perform well, their effectiveness in real-world scenarios is limited by…
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…
HazeFlow: Revisit Haze Physical Model as ODE and Non-Homogeneous Haze Generation for Real-World Dehazing
Junseong Shin, Seungwoo Chung, Yunjeong Yang +1
Dehazing involves removing haze or fog from images to restore clarity and improve visibility by estimating atmospheric scattering effects. While deep learning methods show promise,…
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…