articleOct 1, 2019Closed access

DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better

Ukrainian Catholic University · Texas A&M University

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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…

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Authors

4

Topics & keywords

Keywords
  • Deblurring
  • Computer science
  • Discriminator
  • Block (permutation group theory)
  • Feature (linguistics)
  • Generator (circuit theory)
  • Pyramid (geometry)
  • Image restoration
UN Sustainable Development Goals
  • Reduced inequalities
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