PSF Estimation for Gaussian image blur using backpropagation quantum neural network

摘要：

During spatial remote sensing imaging procedure, combined degradation factors conduce to Gaussian image blurring. The precondition of restoring the degraded image is to estimate point spread function (PSF) of the imaging system as precisely as possible. Because the depredating processes are quite complex, the transfer function of the degraded system is often completely or partly unknown, which makes it quite difficult to identify the precise PSF. Considering the similarity between the quantum process and imaging process in the probability and statistics fields, a novel algorithm is proposed by using multilayer feed-forward back-propagation quantum neural network (QBPNN) to estimate PSF of the Gaussian degraded imaging system. Different from the classical artificial neural network (ANN), 2 adjustable parameters of weight connection coefficient and phase coefficient are introduced in its quantum neurons used in learning stage. By establishing different training sets, this estimation method can overcome the limitation in the dependence on initial values and large amount of computation. Test results show that this method can achieve higher precision, faster convergence and stronger generalization ability comparing with the traditional PSF estimation results.

关键词： Point Spread Function (PSF) Quantum Neural Network (QNN) Guassian blur spatial remote sensing

作者: Kun GAO Yan ZHANG Ying-hui LIU Xiao-mei CHEN Guo-qiang NI

作者单位: Key Laboratory of Photoelectronic Imaging Technology and System, Ministry of Education of China School of Optoelectronics, Beijing Institute of Technology Beijing, China, 100081

会议类型: 国际会议

会议名称: 2010 IEEE 10th International Conference on Signal Processing(第十届信号处理国际会议 ICSP 2010)

会议地点: 北京

会议语种:英文

页码: 1068-1073

在线出版日期: 2010-08-24（万方平台首次上网日期，不代表论文的发表时间）