YeTI: You Only Need Two Noisy Images for Real-World sRGB Noise Generation

Jaekyun Ko; Byung Wan Lim; Soomin Lee; Dongjin Kim; Tae Hyun Kim

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arXiv · 2607.09193

YeTI: You Only Need Two Noisy Images for Real-World sRGB Noise Generation

Jaekyun Ko·Byung Wan Lim·Soomin Lee·Dongjin Kim·Tae Hyun Kim

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Abstract

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 denoisers often overfit to limited paired datasets, while self-supervised methods still depend on sufficiently diverse noisy observations. These limitations motivate scalable noise synthesis methods that can model real-world noise without clean ground truth or camera metadata. We propose YeTI, a real-world sRGB noise generation framework that learns from only two noisy observations of the same scene. YeTI uses a Reconstruction Autoencoder to disentangle scene structure and noise characteristics, and models the latent noise distribution with a one-step Conditional Diffusion Transformer trained using consistency objectives. Given a single noisy input at inference time, YeTI generates realistic, signal-dependent noise while preserving the underlying scene content. Extensive experiments demonstrate the effectiveness of YeTI across real-world benchmarks. We evaluate noise generation on SIDD and further assess generalization on SIDD+, MAI2021, and SID, covering smartphone and diverse consumer-camera sensors. Downstream denoising results on DND further show that denoisers trained with YeTI-synthesized images achieve strong real-world performance, highlighting the practical value of clean-image-free and metadata-free noise generation.

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Jaekyun Ko, Byung Wan Lim, Soomin Lee, Dongjin Kim, Tae Hyun Kim. 2026-07-10. YeTI: You Only Need Two Noisy Images for Real-World sRGB Noise Generation. https://arxiv.org/abs/2607.09193

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