We present an extremely simple Ultra-Resolution Style Transfer framework, termed URST, to flexibly process arbitrary high-resolution images (e.g., 10000x10000 pixels) style transfer for the first time. Most of the existing state-of-the-art methods would fall short due to massive memory cost and small stroke size when processing ultra-high resolution images. URST completely avoids the memory problem caused by ultra-high resolution images by 1) dividing the image into small patches and 2) performing patch-wise style transfer with a novel Thumbnail Instance Normalization (TIN). Specifically, TIN can extract thumbnail's normalization statistics and apply them to small patches, ensuring the style consistency among different patches. Overall, the URST framework has three merits compared to prior arts. 1) We divide input image into small patches and adopt TIN, successfully transferring image style with arbitrary high-resolution. 2) Experiments show that our URST surpasses existing SOTA methods on ultra-high resolution images benefiting from the effectiveness of the proposed stroke perceptual loss in enlarging the stroke size. 3) Our URST can be easily plugged into most existing style transfer methods and directly improve their performance even without training. Code is available at https://github.com/czczup/URST.

本文提出了一种非常简单的超高分辨率风格迁移框架，称为URST，它采用缩略图实例规范化方法在分割的小块上进行风格迁移，从而成功地实现对任意高分辨率图像的风格转移，并利用所提出的笔触感知损失在扩大笔划大小方面超越现有的SOTA方法。

通过缩略图实例归一化实现超分辨率神经风格转移