Photorealism is a complex concept that cannot easily be formulated
mathematically. Deep Photo Style Transfer is an attempt to transfer the style
of a reference image to a content image while preserving its photorealism. This
is achieved by introducing a constraint that prevents distortions in the
content image and by applying the style transfer independently for semantically
different parts of the images. In addition, an automated segmentation process
is presented that consists of a neural network based segmentation method
followed by a semantic grouping step. To further improve the results a measure
for image aesthetics is used and elaborated. If the content and the style image
are sufficiently similar, the result images look very realistic. With the
automation of the image segmentation the pipeline becomes completely
independent from any user interaction, which allows for new applications.

该研究介绍了一种基于深度学习技术的图像风格转换方法，通过引入内容图像的失真约束条件，应用于图像语义不同的部分，使用基于神经网络的图像分割方法和语义分组步骤进行自动分割处理，进一步提高了图像的美感效果，并且使得整个流程不依赖于用户交互的干预，具有广泛的应用前景。