2 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.CV2026
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…
cs.CV2026
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…
cs.CV2023★ 2 cited
Self2Self+: Single-Image Denoising with Self-Supervised Learning and Image Quality Assessment Loss
Jaekyun Ko, Sanghwan Lee
Recently, denoising methods based on supervised learning have exhibited promising performance. However, their reliance on external datasets containing noisy-clean image pairs restr…