YeTI

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

ECCV 2026 · Paper (arXiv:2607.09193) · Code (GitHub) · Dataset

Jaekyun Ko^1,2*, Byung Wan Lim^1*, Soomin Lee¹, Dongjin Kim¹, Tae Hyun Kim^1† <br>¹Department of Computer Science, Hanyang University  ²Mobile eXperience (MX) Division, Samsung Electronics <br>^*equal contribution  ^†corresponding author

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Model Description

YeTI is a real-world sRGB noise generation framework that learns to synthesize realistic, signal-dependent sensor noise from only two noisy observations of the same scene — no clean ground truth or camera metadata required. It uses a Reconstruction Autoencoder (RAE) to disentangle scene structure from noise characteristics, and a one-step Conditional Diffusion Transformer (C-DiT) trained with consistency objectives to model the latent noise distribution. At inference time, YeTI takes a single noisy image and generates additional realistic noisy samples of the same scene, which can be used to train downstream (self-supervised) denoisers such as AP-BSN and MM-BSN.

Full details, training recipe, and evaluation protocol are in the paper and the official code repository.

Intended Uses & Limitations

Intended use: academic research on real-world sRGB noise modeling / synthesis, and as a data-augmentation source for training self-supervised image denoisers. Requires the YeTI code repository to load and run — these checkpoints are not standalone transformers/diffusers models.

Limitations: trained primarily on smartphone sRGB noise (SIDD) and MAI2021 data; noise realism may degrade on sensors, ISO ranges, or capture pipelines far outside the training distribution. Released for academic / non-commercial research use only — see License below.

Files in this repository

Training Data

Trained on the public SIDD (Smartphone Image Denoising Dataset) and MAI2021 benchmarks. Preprocessed archives matching this codebase's expected format are hosted at BWLim/YeTI (dataset).

How to use

These checkpoints are PyTorch Lightning .ckpt files meant to be used with the official YeTI code repository, which defines the model architectures (yeti/archs) and LightningModules (yeti/models) needed to load them.

# 1) Clone the code
git clone https://github.com/ByungWanLim/YeTI.git
cd YeTI

# 2) Download these weights into ckpt/
huggingface-cli download BWLim/YeTI --local-dir ckpt

# 3) Run validation / generation, e.g.
python main.py --config configs/val/C-DiT/val_lit_c-dit.yaml --ckpt ckpt/c_dit.ckpt

See the repository README for the full training / evaluation / noise-generation usage (environment setup, dataset preparation, and all run_*.sh scripts).

Citation

@article{ko2026yeti,
  title   = {YeTI: You Only Need Two Noisy Images for Real-World sRGB Noise Generation},
  author  = {Ko, Jaekyun and Lim, Byung Wan and Lee, Soomin and Kim, Dongjin and Kim, Tae Hyun},
  journal = {arXiv preprint arXiv:2607.09193},
  year    = {2026}
}

License

These model weights are released for academic / non-commercial research use only. See LICENSE for full terms. For commercial licensing inquiries, please contact the corresponding author (Tae Hyun Kim, Hanyang University).