Existing image-to-image translation (I2IT) methods are either constrained to low-resolution images or long inference time due to their heavy computational burden on the convolution of high-resolution feature maps. In this paper, we focus on speeding-up the high-resolution photorealistic I2IT tasks based on closed-form Laplacian pyramid decomposition and reconstruction. Specifically, we reveal that the attribute transformations, such as illumination and color manipulation, relate more to the low-frequency component, while the content details can be adaptively refined on high-frequency components. We consequently propose a Laplacian Pyramid Translation Network (LPTN) to simultaneously perform these two tasks, where we design a lightweight network for translating the low-frequency component with reduced resolution and a progressive masking strategy to efficiently refine the high-frequency ones. Our model avoids most of the heavy computation consumed by processing high-resolution feature maps and faithfully preserves the image details. Extensive experimental results on various tasks demonstrate that the proposed method can translate 4K images in real-time using one normal GPU while achieving comparable transformation performance against existing methods. Datasets and codes are available: https://github.com/csjliang/LPTN.

翻译：现有图像到图像翻译( I2IT) 方法要么限于低分辨率图像,要么由于高分辨率地貌图的演化造成沉重的计算负担,而时间很长,因此,现有的图像到图像翻译( I2IT) 方法要么局限于低分辨率图像,要么由于高分辨率地貌图的演化造成沉重的计算负担,因此时间过长。 在本文件中,我们侧重于加快基于封闭式的拉普拉西亚金字形分解和重建的高分辨率光光光光化的 I2IT 任务。 具体地说, 我们发现, 光化和色彩操纵等属性转换更多地与低频率部分有关, 而内容细节则可以对高频部件进行适应性地改进。 因此,我们提议了一个拉普拉西亚金字塔翻译网络(LPTN), 以同时执行这两项任务, 在那里我们设计了一个轻量网络, 来翻译低频率的分辨率部件, 和一个渐进式遮盖战略, 以高效地改进高频率的部件。 我们的模型避免了处理高分辨率地图和忠实保存图像细节所消耗的大部分重的重算。 各项任务的广泛实验结果表明, 拟议的方法可以实时翻译4K图像, 。 方法可以使用一种正常的GPUPUPU, 同时实现可比较现有的方法: MAp/ Ns 。