基于重参数化多尺度融合网络的高效极暗光原始图像降噪

Practical low-light denoising/enhancement solutions often require fast computation, high memory efficiency, and can achieve visually high-quality restoration results. Most existing methods aim to restore quality but compromise on speed and memory requirements, which limits their usefulness to a large extent. This paper proposes a new deep denoising architecture, a re-parameterized multi-scale fusion network for extreme low-light raw denoising, which greatly improves the inference speed without losing high-quality denoising performance. Specifically, image features are extracted in multi-scale space, and a lightweight spatialchannel parallel attention module is used to focus on core features within space and channel dynamically and adaptively. The representation ability of the model is further enriched by re-parameterized convolutional unit without increasing computational cost at inference. The proposed model can restore UHD 4K resolution images within about 1 s on a CPU(e. g., Intel i7-7700K) and run at 24 fps on a GPU(e. g., NVIDIA GTX 1080Ti), which is almost four times faster than existing advanced methods(e. g., UNet) while still maintaining competitive restoration quality.