Motion-blur parameter estimation of remote sensing image based on quantum neural network

Kun Gao*, Xiao Xian Li, Yan Zhang, Ying Hui Liu

*此作品的通讯作者

科研成果: 书/报告/会议事项章节会议稿件同行评审

2 引用 (Scopus)

摘要

During optical remote sensing imaging procedure, the relative motion between the sensor and the target may corrupt image quality seriously. The precondition of restoring the degraded image is to estimate point spread function (PSF) of the imaging system as precisely as possible. Because of the complexity of the degradation process, the transfer function of the degraded system is often completely or partly unclear, which makes it quite difficult to identify the analytic model of PSF precisely. Inspired by the similarity between the quantum process and imaging process in the probability and statistics fields, one reformed multilayer quantum neural network (QNN) is proposed to estimate PSF of the degraded imaging system. Different from the conventional artificial neural network (ANN), an improved quantum neuron model is used in the hidden layer instead, which introduces a 2-bit controlled NOT quantum gate to control output and 4 texture and edge features as the input vectors. The supervised back-propagation learning rule is adopted to train network based on training sets from the historical images. Test results show that this method owns excellent features of high precision, fast convergence and strong generalization ability.

源语言英语
主期刊名2011 International Conference on Optical Instruments and Technology
主期刊副标题Optoelectronic Imaging and Processing Technology
DOI
出版状态已出版 - 2011
活动2011 International Conference on Optical Instruments and Technology: Optoelectronic Imaging and Processing Technology - Beijing, 中国
期限: 6 11月 20119 11月 2011

出版系列

姓名Proceedings of SPIE - The International Society for Optical Engineering
8200
ISSN(印刷版)0277-786X

会议

会议2011 International Conference on Optical Instruments and Technology: Optoelectronic Imaging and Processing Technology
国家/地区中国
Beijing
时期6/11/119/11/11

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