High-Resolution Photorealistic Image Translation in Real-Time: A Laplacian Pyramid Translation Network

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. 


现有的I2IT的方法被低分辨率图像和冗长的推理时间困扰。在本文中,我们通过闭合拉普拉斯金字塔进行分解和重建以完成高分辨图像的I2IT任务。我们发现光照和色彩变化更多的与图像的低频部分相关,而图像的内容与其高频部分相关。我们在这里提出一种拉普拉斯金字塔变换网络(LPTN), 这个轻量化的网络可以用低分辨率的形式转换低频特征并用一种渐进式的掩膜方式转换高频特征。我们的模型避免的大部分的复杂计算同时保持了尽量多的图像细节。在实验中,我们的模型可以实现实时4k分辨率的图像风格迁移。


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