使用深度神经网络去模糊人物照片

IF 1.2 4区数学 Q2 MATHEMATICS, APPLIED

Inverse Problems and Imaging Pub Date : 2023-01-01 DOI:10.3934/ipi.2022057

Thomas Germer, Tobias Uelwer, Stefan Harmeling

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引用次数: 1

摘要

在本文中，我们提出了赫尔辛基去模糊挑战(HDC2021)的方法。该挑战的任务是在不知道点扩散函数(PSF)的情况下对人物图像进行去模糊处理。组织者提供了一个清晰和模糊图像的数据集。我们的方法包括三个步骤:首先，我们估计图像的扭曲变换，使清晰的图像与模糊的图像对齐。接下来，我们使用准牛顿方法估计PSF。估计的PSF允许产生额外的对清晰和模糊的图像。最后，我们训练了一个深度卷积神经网络从模糊图像中重建出清晰的图像。我们的方法能够成功地重建HDC 2021数据集的前10个阶段的图像。我们的代码可在https://github.com/hhu-machine-learning/hdc2021-psfnn上获得。

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Deblurring photographs of characters using deep neural networks

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 dataset. Our code is available at https://github.com/hhu-machine-learning/hdc2021-psfnn.

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来源期刊

Inverse Problems and Imaging 数学-物理：数学物理

CiteScore

2.50

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： Inverse Problems and Imaging publishes research articles of the highest quality that employ innovative mathematical and modeling techniques to study inverse and imaging problems arising in engineering and other sciences. Every published paper has a strong mathematical orientation employing methods from such areas as control theory, discrete mathematics, differential geometry, harmonic analysis, functional analysis, integral geometry, mathematical physics, numerical analysis, optimization, partial differential equations, and stochastic and statistical methods. The field of applications includes medical and other imaging, nondestructive testing, geophysical prospection and remote sensing as well as image analysis and image processing. This journal is committed to recording important new results in its field and will maintain the highest standards of innovation and quality. To be published in this journal, a paper must be correct, novel, nontrivial and of interest to a substantial number of researchers and readers.