Deblurring via Stochastic Refinement

@article{Whang2021DeblurringVS,
  title={Deblurring via Stochastic Refinement},
  author={Jay Whang and Mauricio Delbracio and Hossein Talebi and Chitwan Saharia and Alexandros G. Dimakis and Peyman Milanfar},
  journal={2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2021},
  pages={16272-16282}
}
  • Jay WhangM. Delbracio P. Milanfar
  • Published 5 December 2021
  • Computer Science
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Image deblurring is an ill-posed problem with multiple plausible solutions for a given input image. However, most existing methods produce a deterministic estimate of the clean image and are trained to minimize pixel-level distortion. These metrics are known to be poorly correlated with human perception, and often lead to unrealistic reconstructions. We present an alternative framework for blind deblurring based on conditional diffusion models. Unlike existing techniques, we train a stochastic… 

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