We address personalized image enhancement in this study, where we enhance
input images for each user based on the user's preferred images. Previous
methods apply the same preferred style to all input images (i.e., only one
style for each user); in contrast to these methods, we aim to achieve
content-aware personalization by applying different styles to each image
considering the contents. For content-aware personalization, we make two
contributions. First, we propose a method named masked style modeling, which
can predict a style for an input image considering the contents by using the
framework of masked language modeling. Second, to allow this model to consider
the contents of images, we propose a novel training scheme where we download
images from Flickr and create pseudo input and retouched image pairs using a
degrading model. We conduct quantitative evaluations and a user study, and our
method trained using our training scheme successfully achieves content-aware
personalization; moreover, our method outperforms other previous methods in
this field. Our source code is available at
this https URL

本研究提出了一种个性化图像增强方法，使用遮蔽风格建模技术为每个用户个性化地增强输入图像并在 Flickr 数据集上进行训练，实现了基于内容的个性化图像增强，并在用户研究中证明了优于其他方法的效果。