We present an end-to-end learning approach for motion deblurring, which is based on conditional GAN and content loss. It improves the state-of-the art in terms of peak signal-to-noise ratio, structural similarity measure and by visual appearance. The quality of the deblurring model is also evaluated in a novel way on a real-world problem -- object detection on (de-)blurred images. The method is 5 times faster than the closest competitor. Second, we present a novel method of generating synthetic motion blurred images from the sharp ones, which allows realistic dataset augmentation. Model, training code and dataset are available at https://github.com/KupynOrest/DeblurGAN

翻译：我们提出了一个关于运动分流的端到端学习方法,该方法以有条件的GAN和内容损失为基础,它改进了最高信号-噪音比率、结构相似度测量和视觉外观方面的先进水平,还以新颖的方式评估了分流模型的质量,即对(de-)blururred图像的物体探测,比最接近的竞争者快5倍。第二,我们提出了一种新型方法,从锐利的图像中生成合成运动模糊的图像,这可以增加现实的数据。模型、培训代码和数据集可在https://github.com/KupynOrest/DeblurGAN上查阅。