In this paper, we introduce NBNet, a novel framework for image denoising. Unlike previous works, we propose to tackle this challenging problem from a new perspective: noise reduction by image-adaptive projection. Specifically, we propose to train a network that can separate signal and noise by learning a set of reconstruction basis in the feature space. Subsequently, image denosing can be achieved by selecting corresponding basis of the signal subspace and projecting the input into such space. Our key insight is that projection can naturally maintain the local structure of input signal, especially for areas with low light or weak textures. Towards this end, we propose SSA, a non-local subspace attention module designed explicitly to learn the basis generation as well as the subspace projection. We further incorporate SSA with NBNet, a UNet structured network designed for end-to-end image denosing. We conduct evaluations on benchmarks, including SIDD and DND, and NBNet achieves state-of-the-art performance on PSNR and SSIM with significantly less computational cost.

本文介绍了NBNet，这是一种新的图像去噪框架，该框架使用图像自适应的投影来减少噪声，并且通过学习一组特征空间中的重构基来区分训练网络，进而使用相应的信号子空间基和投影进行图像去噪和降噪。我们提出的SSA非局部子空间注意力模块也得到了验证，并与UNet结构的NBNet融合，该模型在SIDD和DND等基准测试上取得了良好的性能。

NBNet: 基于噪声基学习和子空间投影的图像去噪