标题： YOND：摆脱相机特定数据依赖的实用盲原始图像去噪方法 显示英文标题

标题： YOND: Practical Blind Raw Image Denoising Free from Camera-Specific Data Dependency

摘要： 摄影技术的快速发展催生了对实用盲原始图像去噪方法日益增长的需求。近期，基于学习的方法因其卓越的性能已成为主流。然而，大多数现有的基于学习的方法都存在对特定相机数据依赖的问题，导致在应用于未知相机的数据时性能下降。为了解决这一挑战，我们引入了一种名为YOND（You Only Need a Denoiser）的新颖盲原始图像去噪方法。YOND仅使用合成数据进行训练，但能够稳健地推广到由各种未知相机捕获的嘈杂原始图像上。 具体来说，我们提出了三个关键模块以确保YOND的实用性：粗到细噪声估计（CNE）、期望匹配方差稳定变换（EM-VST）和信噪比引导去噪器（SNR-Net）。首先，我们提出CNE来识别相机的噪声特性，并根据粗略去噪后的图像优化噪声参数估计。其次，我们提出EM-VST消除特定相机数据依赖性，根据噪声图像校正VST的偏差期望。最后，我们提出SNR-Net以提供可控的原始图像去噪功能，支持自适应调整和手动微调。 在未知相机上的广泛实验以及针对具有挑战性情况的灵活解决方案表明了我们方法的优越实用性。源代码将在\href{https://fenghansen.github.io/publication/YOND}{项目主页}公开发布。 显示英文摘要

摘要： 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}.