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运动与离焦耦合的模糊图像参数辨识方法
引用本文:刘子伟,许廷发,赵鹏.运动与离焦耦合的模糊图像参数辨识方法[J].北京理工大学学报,2014,34(3):327-330.
作者姓名:刘子伟  许廷发  赵鹏
作者单位:北京理工大学光电学院光电成像技术与系统教育部重点实验室,北京 100081;东北林业大学信息与计算机工程学院,黑龙江,哈尔滨150040
基金项目:国家自然科学基金重点资助项目(61027002);国家“九七三”计划项目(2009CB72400603);国家自然科学基金资助项目(60972100);国家教育部新世纪优秀人才支持计划专项资助项目(NCET-12-0809)
摘    要:针对运动和离焦耦合模糊图像运动模糊尺度、角度和离焦模糊半径的辨识难题,提出了一种运动与离焦耦合的模糊图像参数辨识方法. 首先,对模糊图像进行Fourier变换,采用Radon变换辨识运动模糊角度;其次,对模糊图像的频谱图进行滤波,将频谱图中央区域的幅度之和作为BP神经网络的输入量,检测运动模糊尺度时,频谱图按列求和,而检测离焦模糊半径时,频谱图按圆形方向求和. 仿真实验表明,不带噪声的耦合模糊图像参数辨识误差在6%以内. 

关 键 词:运动模糊  离焦模糊  神经网络  Radon变换
收稿时间:2012/12/7 0:00:00

Parameter Identification for Mixed Blur Image with Motion Blur and Defocus Blur
LIU Zi-wei,XU Ting-fa and ZHAO Peng.Parameter Identification for Mixed Blur Image with Motion Blur and Defocus Blur[J].Journal of Beijing Institute of Technology(Natural Science Edition),2014,34(3):327-330.
Authors:LIU Zi-wei  XU Ting-fa and ZHAO Peng
Institution:1.Key Laboratory of Photoelectronic Imaging Technology and System, Ministry of Education of China, School of Optoelectronics, Beijing Institute of Technology, Beijing 100081, China2.Information and Computer Engineering College, Northeast Forestry University, Harbin, Heilongjiang 150040, China
Abstract:For the problem of identifying motion blur length, motion blur angle and defocus blur radius in mixed blur image, a method for parameter identification for mixed blur image with motion blur and defocus blur was proposed. Firstly, the blur image was processed with Fourier transformation, then Radon transformation was adopted to identify motion blur angle. Secondly, filter was adopted for the spectrum, the amplitudes in the central region of the spectrum were summed, and the sum was inputted into the BP neural network. The amplitudes were summed up vertically when estimating motion blur length and were summed up circularly when estimating defocus blur length. The simulation results show that the error of parameter identifications of noise free mixed blur images is smaller than 6%.
Keywords:motion blur  defocus blur  neural network  Radon transformation
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