YOND: Practical Blind Raw Image Denoising Free from Camera-Specific Data Dependency
Journal:
arXiv
Published Date:
Jun 4, 2025
Abstract
The rapid advancement of photography has created a growing demand for a
practical blind raw image denoising method. Recently, learning-based methods
have become mainstream due to their excellent performance. However, most
existing learning-based methods suffer from camera-specific data dependency,
resulting in performance drops when applied to data from unknown cameras. To
address this challenge, we introduce a novel blind raw image denoising method
named YOND, which represents You Only Need a Denoiser. Trained solely on
synthetic data, YOND can generalize robustly to noisy raw images captured by
diverse unknown cameras. Specifically, we propose three key modules to
guarantee the practicality of YOND: coarse-to-fine noise estimation (CNE),
expectation-matched variance-stabilizing transform (EM-VST), and SNR-guided
denoiser (SNR-Net). Firstly, we propose CNE to identify the camera noise
characteristic, refining the estimated noise parameters based on the coarse
denoised image. Secondly, we propose EM-VST to eliminate camera-specific data
dependency, correcting the bias expectation of VST according to the noisy
image. Finally, we propose SNR-Net to offer controllable raw image denoising,
supporting adaptive adjustments and manual fine-tuning. Extensive experiments
on unknown cameras, along with flexible solutions for challenging cases,
demonstrate the superior practicality of our method. The source code will be
publicly available at the
\href{https://fenghansen.github.io/publication/YOND}{project homepage}.
