We present a new end-to-end generative adversarial network (GAN) for single
image motion deblurring, named DeblurGAN-v2, which considerably boosts
state-of-the-art deblurring efficiency, quality, and flexibility. DeblurGAN-v2
is based on a relativistic conditional GAN with a double-scale discriminator.
For the first time, we introduce the Feature Pyramid Network into deblurring,
as a core building block in the generator of DeblurGAN-v2. It can flexibly work
with a wide range of backbones, to navigate the balance between performance and
efficiency. The plug-in of sophisticated backbones (e.g., Inception-ResNet-v2)
can lead to solid state-of-the-art deblurring. Meanwhile, with light-weight
backbones (e.g., MobileNet and its variants), DeblurGAN-v2 reaches 10-100 times
faster than the nearest competitors, while maintaining close to
state-of-the-art results, implying the option of real-time video deblurring. We
demonstrate that DeblurGAN-v2 obtains very competitive performance on several
popular benchmarks, in terms of deblurring quality (both objective and
subjective), as well as efficiency. Besides, we show the architecture to be
effective for general image restoration tasks too. Our codes, models and data
are available at: this https URL

DeblurGAN-v2 是一种基于到相对论条件 GAN 的新型端到端生成对抗网络，其采用 Feature Pyramid Network 作为生成器中的核心构建块，在单幅图像运动去模糊方面有着很高的实用价值。