Blur2Blur: Blur Conversion for Unsupervised Image Deblurring on Unknown
  Domains
Bang-Dang Pham Phong Tran Anh Tran Cuong Pham Rang Nguyen Minh Hoai
This paper presents an innovative framework designed to train an image
deblurring algorithm tailored to a specific camera device. This algorithm works
by transforming a blurry input image, which is challenging to deblur, into
another blurry image that is more amenable to deblurring. The transformation
process, from one blurry state to another, leverages unpaired data consisting
of sharp and blurry images captured by the target camera device. Learning this
blur-to-blur transformation is inherently simpler than direct blur-to-sharp
conversion, as it primarily involves modifying blur patterns rather than the
intricate task of reconstructing fine image details. The efficacy of the
proposed approach has been demonstrated through comprehensive experiments on
various benchmarks, where it significantly outperforms state-of-the-art methods
both quantitatively and qualitatively. Our code and data are available at
https://zero1778.github.io/blur2blur/