Image denoising is a critical component in a camera's Image Signal Processing (ISP) pipeline. There are two typical ways to inject a denoiser into the ISP pipeline: applying a denoiser directly to captured raw frames (raw domain) or to the ISP's output sRGB images (sRGB domain). However, both approaches have their limitations. Residual noise from raw-domain denoising can be amplified by the subsequent ISP processing, and the sRGB domain struggles to handle spatially varying noise since it only sees noise distorted by the ISP. Consequently, most raw or sRGB domain denoising works only for specific noise distributions and ISP configurations. To address these challenges, we propose DualDn, a novel learning-based dual-domain denoising. Unlike previous single-domain denoising, DualDn consists of two denoising networks: one in the raw domain and one in the sRGB domain. The raw domain denoising adapts to sensor-specific noise as well as spatially varying noise levels, while the sRGB domain denoising adapts to ISP variations and removes residual noise amplified by the ISP. Both denoising networks are connected with a differentiable ISP, which is trained end-to-end and discarded during the inference stage. With this design, DualDn achieves greater generalizability compared to most learning-based denoising methods, as it can adapt to different unseen noises, ISP parameters, and even novel ISP pipelines. Experiments show that DualDn achieves state-of-the-art performance and can adapt to different denoising architectures. Moreover, DualDn can be used as a plug-and-play denoising module with real cameras without retraining, and still demonstrate better performance than commercial on-camera denoising. The project website is available at: https://openimaginglab.github.io/DualDn/

翻译：图像去噪是相机图像信号处理（ISP）流水线中的关键组成部分。将去噪器注入ISP流水线有两种典型方式：直接对捕获的原始帧（原始域）或对ISP输出的sRGB图像（sRGB域）应用去噪器。然而，这两种方法均存在局限性。原始域去噪的残留噪声可能被后续ISP处理放大，而sRGB域由于仅能观察到经ISP畸变后的噪声，难以处理空间变化的噪声。因此，大多数原始域或sRGB域去噪方法仅适用于特定的噪声分布和ISP配置。为应对这些挑战，我们提出DualDn，一种新颖的基于学习的双域去噪方法。与以往的单域去噪不同，DualDn包含两个去噪网络：一个在原始域，另一个在sRGB域。原始域去噪适应传感器特定噪声及空间变化的噪声水平，而sRGB域去噪则适应ISP变化并消除被ISP放大的残留噪声。两个去噪网络通过一个可微分ISP连接，该ISP在端到端训练中使用，在推理阶段被丢弃。通过此设计，DualDn相较于大多数基于学习的去噪方法具有更强的泛化能力，能够适应不同的未知噪声、ISP参数，甚至全新的ISP流水线。实验表明，DualDn实现了最先进的性能，并能适配不同的去噪架构。此外，DualDn可作为即插即用的去噪模块与真实相机配合使用而无需重新训练，其性能仍优于商业相机内置去噪方案。项目网站位于：https://openimaginglab.github.io/DualDn/