  The dominant image-to-image translation methods are based on fully
convolutional networks, which extract and translate an image's features and
then reconstruct the image. However, they have unacceptable computational costs
when working with high-resolution images. To this end, we present the
Multi-Curve Translator (MCT), which not only predicts the translated pixels for
the corresponding input pixels but also for their neighboring pixels. And if a
high-resolution image is downsampled to its low-resolution version, the lost
pixels are the remaining pixels' neighboring pixels. So MCT makes it possible
to feed the network only the downsampled image to perform the mapping for the
full-resolution image, which can dramatically lower the computational cost.
Besides, MCT is a plug-in approach that utilizes existing base models and
requires only replacing their output layers. Experiments demonstrate that the
MCT variants can process 4K images in real-time and achieve comparable or even
better performance than the base models on various photorealistic
image-to-image translation tasks.
