论文标题
高分辨率影像图像翻译的多曲线翻译器
Multi-Curve Translator for High-Resolution Photorealistic Image Translation
论文作者
论文摘要
主要的图像到图像转换方法基于完全卷积的网络，该网络提取和翻译图像的特征，然后重建图像。但是，使用高分辨率图像时，它们的计算成本不可接受。为此，我们介绍了多曲线翻译器（MCT），它不仅可以预测相应的输入像素的翻译像素，还可以预测其相邻像素的翻译像素。而且，如果将高分辨率图像删除到其低分辨率版本中，则丢失的像素是其余像素的相邻像素。因此，MCT可以使网络仅馈入倒数采样的图像以执行完整分辨率图像的映射，这可以大大降低计算成本。此外，MCT是一种使用现有基本型号的插件方法，仅需要更换其输出层。实验表明，MCT变体可以实时处理4K图像，并比各种逼真的图像到图像翻译任务上的基本模型实现可比甚至更好的性能。
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.
