Image Denoising via Style Disentanglement
Jingwei Niu Jun Cheng Shan Tan
Image denoising is a fundamental task in low-level computer vision. While
recent deep learning-based image denoising methods have achieved impressive
performance, they are black-box models and the underlying denoising principle
remains unclear. In this paper, we propose a novel approach to image denoising
that offers both clear denoising mechanism and good performance. We view noise
as a type of image style and remove it by incorporating noise-free styles
derived from clean images. To achieve this, we design novel losses and network
modules to extract noisy styles from noisy images and noise-free styles from
clean images. The noise-free style induces low-response activations for noise
features and high-response activations for content features in the feature
space. This leads to the separation of clean contents from noise, effectively
denoising the image. Unlike disentanglement-based image editing tasks that edit
semantic-level attributes using styles, our main contribution lies in editing
pixel-level attributes through global noise-free styles. We conduct extensive
experiments on synthetic noise removal and real-world image denoising datasets
(SIDD and DND), demonstrating the effectiveness of our method in terms of both
PSNR and SSIM metrics. Moreover, we experimentally validate that our method
offers good interpretability.