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
this https URL

本研究提出了一种创新的框架，旨在训练一个针对特定相机设备的图像去模糊算法，该算法通过将难以去模糊的模糊输入图像转化为更加易于去模糊的另一个模糊图像来工作。该模型基于一个模糊到模糊的转换过程，利用目标相机设备捕获的清晰和模糊图像的非配对数据。通过各种基准测试的全面实验证明了所提方法的有效性，定量和定性上都显著优于现有的方法。本研究的源代码和数据可在链接中获取。