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cs.CV2026
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…
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
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…