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.

通过提取噪音图片和清晰图片的噪音样式和无噪音样式，并利用低响应噪音特征和高响应内容特征的特征空间，从而分离干净内容和噪音，实现像素级属性编辑的图像去噪方法。在合成噪音去除和真实世界图像去噪数据集（SIDD 和 DND）上的广泛实验证明了该方法在 PSNR 和 SSIM 度量方面的有效性，并且在实验中验证了该方法具有良好的可解释性。

图像去噪：基于样式解缠的方法

Image Denoising via Style Disentanglement

Though convolutional neural networks (CNNs) have demonstrated remarkable
ability in learning discriminative features, they often generalize poorly to
unseen domains. Domain generalization aims to address this problem by learning
from a set of source domains a model that is generalizable to any unseen
domain. In this paper, a novel approach is proposed based on probabilistically
mixing instance-level feature statistics of training samples across source
domains. Our method, termed MixStyle, is motivated by the observation that
visual domain is closely related to image style (e.g., photo vs.~sketch
images). Such style information is captured by the bottom layers of a CNN where
our proposed style-mixing takes place. Mixing styles of training instances
results in novel domains being synthesized implicitly, which increase the
domain diversity of the source domains, and hence the generalizability of the
trained model. MixStyle fits into mini-batch training perfectly and is
extremely easy to implement. The effectiveness of MixStyle is demonstrated on a
wide range of tasks including category classification, instance retrieval and
reinforcement learning.

本研究介绍了一种新的基于概率混合实例级别特征统计的域泛化方法 MixStyle 来提高卷积神经网络模型的泛化性能，该方法着眼于图像风格与视觉域之间的关联关系，并将其应用于 CNN 的底层进行数据增广，既可用于分类任务还可应用于实例检索和强化学习等任务。

混合样式领域泛化

Domain Generalization with MixStyle

We propose a new neural network architecture for solving single-image
analogies - the generation of an entire set of stylistically similar images
from just a single input image. Solving this problem requires separating image
style from content. Our network is a modified variational autoencoder (VAE)
that supports supervised training of single-image analogies and in-network
evaluation of outputs with a structured similarity objective that captures
pixel covariances. On the challenging task of generating a 62-letter font from
a single example letter we produce images with 22.4% lower dissimilarity to the
ground truth than state-of-the-art.

提出了一种新的神经网络结构来解决单张图像类比的问题，我们的网络是一个修改后的变分自编码器（VAE），支持有监督的训练和结构相似性目标的输出评估，通过对于 62 个字体从单个样例字母的生成展示出比现有技术更优异的结果。