Research on motion deblurring from aero-photograph

doi:10.3778/j.issn.1002-8331.2010.33.049

Computer Engineering and Applications ›› 2010, Vol. 46 ›› Issue (33): 175-177.DOI: 10.3778/j.issn.1002-8331.2010.33.049

• 图形、图像、模式识别 • Previous Articles Next Articles

Research on motion deblurring from aero-photograph

ZHANG Hui-hui^1，2，WANG Xiao-tong^1，2，XU Xiao-gang^1，3，XU Guan-lei^1，2

1.Laboratory of Photo Electricity Technology，Dalian Naval Academy，Dalian，Liaoning 116018，China
2.Department of Navigation，Dalian Naval Academy，Dalian，Liaoning 116018，China
3.Department of Automatization，Dalian Naval Academy，Dalian，Liaoning 116018，China

Received:2009-03-31 Revised:2009-06-01 Online:2010-11-21 Published:2010-11-21
Contact: ZHANG Hui-hui

航空影像去运动模糊研究

张辉辉^1，2，王孝通^1，2，徐晓刚^1，3，徐冠雷^1，2

1.海军大连舰艇学院光电技术研究所，辽宁大连 116018
2.海军大连舰艇学院航海系，辽宁大连 116018
3.海军大连舰艇学院装备系统与自动化系，辽宁大连 116018

通讯作者: 张辉辉

Abstract

Abstract: The gradient distribution of aero-photograph is analyzed.An improved method of estimating blur kernel and BRL（Bilateral Richardson-Lucy） deconvolution algorithm are introduced.Then，an image restoration method of motion-deblurring from an aero-photograph is presented，and used to process some motion-blurred aero-photographs.Experiment results show that the image displacement is removed effectively，and the vision quality of aero-photographs is improved obviously by using this method.Especially the important edges are preserved.

Key words: aero-photograph, gradient distribution, motion-deblurring, deconvolution

摘要： 分析了航空影像梯度分布特征，介绍了一种改进的模糊核估计方法和BRL（Bilateral Richardson-Lucy）反卷积算法，并提出了一种同时具有模糊核估计和BRL反卷积功能的单幅航空影像去除运动模糊方法，最后用运动模糊航空影像进行了实验验证和分析。结果表明，该方法可有效去除像移，改善模糊航空影像视觉效果，特别是可以保护图像中重要边缘信息。

关键词: 航空影像, 梯度分布, 去运动模糊, 反卷积

CLC Number:

TP911.73

ZHANG Hui-hui^1，2，WANG Xiao-tong^1，2，XU Xiao-gang^1，3，XU Guan-lei^1，2. Research on motion deblurring from aero-photograph[J]. Computer Engineering and Applications, 2010, 46(33): 175-177.

张辉辉^1，2，王孝通^1，2，徐晓刚^1，3，徐冠雷^1，2. 航空影像去运动模糊研究[J]. 计算机工程与应用, 2010, 46(33): 175-177.

[1]	WANG Yunyan, LUO Lengkun, ZHOU Zhigang. Remote Sensing Image Classification Based on High-Level Features of Deconvolution [J]. Computer Engineering and Applications, 2020, 56(11): 200-206.
[2]	WANG Zhen, SHI Yun, LI Yubin. Segmentation of Corn Leaf Diseases Based on Improved Fully Convolutional Neural Network [J]. Computer Engineering and Applications, 2019, 55(22): 127-132.
[3]	WU Jiaxin, HU Xiaohui, ZHANG Ming. Image Super-Resolution Algorithm Based on Composite Convolutional Neural Network [J]. Computer Engineering and Applications, 2019, 55(13): 186-192.
[4]	ZENG Jiexian, NI Shenlong. Improved Super-Resolution Reconstruction of Single Image Based on Convolution Neural Network [J]. Computer Engineering and Applications, 2019, 55(13): 1-7.
[5]	ZHANG Huan1，2, LEI Hong1, CHEN Kaiqiang1，2. Application of densely connected deconvolutional neural network in building extraction from remote sensing images [J]. Computer Engineering and Applications, 2018, 54(11): 140-144.
[6]	XIAO Su, ZHENG Ying. Sparse representation based multi-regularized image deconvolution [J]. Computer Engineering and Applications, 2015, 51(12): 203-207.
[7]	NIE Duxian. Multi-frame images blind deconvolution algorithm based on non local total variation regularization [J]. Computer Engineering and Applications, 2014, 50(1): 156-158.
[8]	YANG Shunliao. Application of homomorphism deconvolution processing in pitch detection [J]. Computer Engineering and Applications, 2013, 49(24): 214-218.
[9]	hanyu hong. Research on Algorithm of Alternant Iterative blind Restoration Based on Conjugate Gradient Method of Frequency Domain [J]. Computer Engineering and Applications, 2007, 43(2期): 5-5.

Research on motion deblurring from aero-photograph

航空影像去运动模糊研究

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 9

Recommended Articles

Metrics