论文标题
使用深神经网络的人物的脱张照片
Deblurring Photographs of Characters Using Deep Neural Networks
论文作者
论文摘要
在本文中，我们介绍了赫尔辛基DeBlur挑战赛（HDC2021）的方法。这项挑战的任务是在不知道点扩展功能（PSF）的情况下删除字符的图像。组织者提供了一对尖锐和模糊的图像的数据集。我们的方法包括三个步骤：首先，我们估计图像的翘曲转换，以使锋利的图像与模糊图像对齐。接下来，我们使用准Newton方法估算PSF。估计的PSF允许生成其他成对的尖锐和模糊的图像。最后，我们训练一个深度卷积神经网络，从模糊的图像中重建尖锐的图像。我们的方法能够从HDC 2021数据的前10个阶段成功重建图像。我们的代码可在https://github.com/hhu-machine-learning/hdc2021-psfnn上找到。
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 https://github.com/hhu-machine-learning/hdc2021-psfnn.
