“One-Shot” Super-Resolution via Backward Style Transfer for Fast High-Resolution Style Transfer

Abstract

Owing to the excellent visual quality of results, Gatys et al.'s Neural Style Transfer (NST) online algorithm is regarded as the gold-standard in the community of NST, but this algorithm is quite time-consuming especially for high-resolution (HR) image. In this letter, we propose “One-Shot” super-resolution (SR) for fast high-resolution style transfer. We first generate a low-resolution (LR) stylized image by NST, and then use “One-Shot” super-resolution to restore the HR stylized image by learning the mapping relations between HR-LR stylized images from HR-LR style images. However, due to the style loss is not eliminated, there are some subtle but important fine-grained style differences between LR stylized and style images. These differences lead to the poor visual quality of SR results. To reduce the style differences further, we adjust the texture of LR style image to approach LR stylized image by backward style transfer. The result of backward style transfer will be treated as the LR part of the “One-Shot” example pair, which leads to a better SR. The experimental results show that with good visual quality, our method reduces the time consumption by 81.6%. Especially in a specific application scenario of fixed style image and changed content image, our method reduces the time consumption by 89.3%.