  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.
