DeblurGAN: Blind motion deblurring using conditional adversarial networks
|Titel||DeblurGAN: Blind motion deblurring using conditional adversarial networks|
|Buchtitel||Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2018)|
|Ort||IEEE Conference on Computer Vision and Pattern Recognition (CVPR)|
We present DeblurGAN, an end-to-end learned method for motion deblurring. The learning is based on a conditional GAN and the content loss . DeblurGAN achieves state-of-the art performance both in the structural similarity measure and 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 -- DeepDeblur. We also introduce a novel method for generating synthetic motion blurred images from sharp ones, allowing realistic dataset augmentation.