DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better
Ukrainian Catholic University · Texas A&M University
Abstract
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 performance while being much more flexible and efficient. 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 performance. Meanwhile, with light-weight…
Citation impact
- FWCI
- 43.89
- Percentile
- 100%
- References
- 81
Authors
4Topics & keywords
- Deblurring
- Computer science
- Discriminator
- Block (permutation group theory)
- Feature (linguistics)
- Generator (circuit theory)
- Pyramid (geometry)
- Image restoration
- Reduced inequalities