A Semi-blind Restoration Method of UAV Image Considering the Blurred Kernel Connectivity

被引：0
|
作者：
Li J. [1 ]
Wu H. [1 ]
Lin Y. [2 ]
Gao P. [1 ]
Wang W. [1 ]
A X. [1 ]
Yan L. [1 ]
机构：
[1] Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming
[2] Institute of Information Technology and Cyber Security, Peoples' Public Security University of China, Beijing
来源：
Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University | 2021年 / 46卷 / 06期
基金：
中国国家自然科学基金;
关键词：
Blurred kernel connectivity; Gradient screening; Semi-blind restoration; Split Bregman algorithm;
D O I：
10.13203/j.whugis20190160
中图分类号：
学科分类号：
摘要：
Objectives: Restoration of blurred aerial image can improve the details and features of images, and enhance the recognition capability and positioning accuracy of targets.Methods: A semi-blind restoration method of unmanned aerial vehicle image considering blurred kernel connectivity is proposed, which uses existing aerial images to establish the model of blurred kernel estimation under the conditions of non-parameters. Firstly, the gradient screening is established to filter out the public features of the blurred image and the existing clear image gradient domain, and construct the fidelity term. Then we use eight neighborhoods of blurred kernel gradient to describe the connectivity measurement of the blurred kernel and use it as a regular term to reduce the solution space and build the model. Finally, we reconstruct the image, estimate the blurred kernel hierarchically according to the image pyramid structure, and reconstruct the image by deconvolution with the split Bregman algorithm.Results: The experiment analyzes and evaluates the proposed method from four aspects: fuzzy type, public ground objects, comparison of methods and applicability of the method. Compared to the existing methods, the experiment results show that, when the public ground objects are over 35%, the blurred aerial images can be restored effectively.Conclusions: Compared with the existing image restoration methods, our proposed method has faster convergence speed, higher accuracy of fuzzy kernel, clearer details and richer information of restored image. © 2021, Editorial Board of Geomatics and Information Science of Wuhan University. All right reserved.
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页码：816 / 824
页数：8
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