Creating fine-retouched portrait images is tedious and time-consuming even
for professional artists. There exist automatic retouching methods, but they
either suffer from over-smoothing artifacts or lack generalization ability. To
address such issues, we present StyleRetoucher, a novel automatic portrait
image retouching framework, leveraging StyleGAN's generation and generalization
ability to improve an input portrait image's skin condition while preserving
its facial details. Harnessing the priors of pretrained StyleGAN, our method
shows superior robustness: a). performing stably with fewer training samples
and b). generalizing well on the out-domain data. Moreover, by blending the
spatial features of the input image and intermediate features of the StyleGAN
layers, our method preserves the input characteristics to the largest extent.
We further propose a novel blemish-aware feature selection mechanism to
effectively identify and remove the skin blemishes, improving the image skin
condition. Qualitative and quantitative evaluations validate the great
generalization capability of our method. Further experiments show
StyleRetoucher's superior performance to the alternative solutions in the image
retouching task. We also conduct a user perceptive study to confirm the
superior retouching performance of our method over the existing
state-of-the-art alternatives.

通过利用 StyleGAN 的生成和概括能力改善输入肖像图像的肤色状况，同时保留其面部细节，我们提出了一种新颖的自动肖像图像修饰框架 StyleRetoucher。与现有替代方案相比，我们方法表现出更好的泛化能力，在肖像图像修饰任务中具有优越的性能。

StyleRetoucher: 带有 GAN 先验的通用肖像图像修饰

StyleRetoucher: Generalized Portrait Image Retouching with GAN Priors

Despite the recent success in applying supervised deep learning to medical
imaging tasks, the problem of obtaining large and diverse expert-annotated
datasets required for the development of high performant models remains
particularly challenging. In this work, we explore the possibility of using
Generative Adverserial Networks (GAN) to synthesize clinical images with skin
condition. We propose DermGAN, an adaptation of the popular Pix2Pix
architecture, to create synthetic images for a pre-specified skin condition
while being able to vary its size, location and the underlying skin color. We
demonstrate that the generated images are of high fidelity using objective GAN
evaluation metrics. In a Human Turing test, we note that the synthetic images
are not only visually similar to real images, but also embody the respective
skin condition in dermatologists' eyes. Finally, when using the synthetic
images as a data augmentation technique for training a skin condition
classifier, we observe that the model performs comparably to the baseline model
overall while improving on rare but malignant conditions.

本研究使用生成式对抗网络（GAN）创建临床图像合成，提出了适应于特定皮肤病状的 DermGAN，并使用合成图像作为数据增强技术，提高了皮肤病分类器的性能。