In this paper, we present our approach for the Helsinki Deblur Challenge (HDC2021). The task of this challenge is to deblur images of characters without knowing the point spread function (PSF). The organizers provided a dataset of pairs of sharp and blurred images. Our method consists of three steps: First, we estimate a warping transformation of the images to align the sharp images with the blurred ones. Next, we estimate the PSF using a quasi-Newton method. The estimated PSF allows to generate additional pairs of sharp and blurred images. Finally, we train a deep convolutional neural network to reconstruct the sharp images from the blurred images. Our method is able to successfully reconstruct images from the first 10 stages of the HDC 2021 data. Our code is available at \url{https://github.com/hhu-machine-learning/hdc2021-psfnn}.

翻译：在本文中,我们展示了我们在赫尔辛基Deblur挑战(HDC2021)中的方法。这项挑战的任务是在不知道点扩散功能(PSF)的情况下对字符的图像进行破坏。组织者提供了一对锐利和模糊图像的数据集。我们的方法由三个步骤组成:首先,我们估计图像的扭曲变换,以便与模糊图像相匹配。接下来,我们使用准Newton方法对PSF进行估计。估计的PSF可以产生另外一对锐利和模糊的图像。最后,我们训练了一个深革命神经网络,以从模糊图像中重建锐利图像。我们的方法能够成功地从 HDC 2021 前10个阶段的数据中重建图像。我们的代码可以在\url{https://github.com/hhu-merch-learning/hdc2021-psfnn}查阅。