Blind deconvolution is solved by alternating gradient descent in the latent spaces of pretrained image and blur-kernel generators, with a slack variant that relaxes the image constraint.
DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
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. The model, code and the dataset are available at https://github.com/KupynOrest/DeblurGAN
fields
cs.CV 1years
2019 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Blind Image Deconvolution using Pretrained Generative Priors
Blind deconvolution is solved by alternating gradient descent in the latent spaces of pretrained image and blur-kernel generators, with a slack variant that relaxes the image constraint.