NBNet: Noise Basis Learning for Image Denoising with Subspace Projection

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 attention module we design to explicitly learn the basis generation as well as subspace projection. We further incorporate SSA with NBNet, a UNet structured network designed for end-to-end image denosing based. 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.

1. Introduction

Image denoising is a fundamental and long lasting task in image processing and computer vision. The main challenging is to recover a clean signal x from the noisy observation y, with the additive noise n, namely: $$\begin{equation*}{\mathbf{y}} = {\mathbf{x}} + {\mathbf{n}}\tag{1}\end{equation*}$$