  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.
