Identification of robust blur parameter for uniform linear motion blurred images

ZHAO Peng1; 2; CAO Jun2; WEI Xing-zhu1

doi:10.3788/OPE.20132109.2430

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Identification of robust blur parameter for uniform linear motion blurred images

更新时间：2020-08-12

- Identification of robust blur parameter for uniform linear motion blurred images
- Optics and Precision Engineering Vol. 21, Issue 9, Pages: 2430-2438(2013)
- 作者机构：
  
  1. 东北林业大学信息与计算机工程学院，黑龙江哈尔滨 150040
  2. 东北林业大学机械工程博士后流动站，黑龙江哈尔滨 150040
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20132109.2430
  CLC： TP391.4
- Received：31 March 2013，
  
  Revised：23 April 2013，
  
  Published Online：30 September 2013，
  
  Published：15 September 2013
- 稿件说明：
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赵鹏曹军韦兴竹. 匀速直线运动模糊图像的模糊参数鲁棒识别[J]. 光学精密工程, 2013,21(9): 2430-2438

ZHAO Peng CAO Jun WEI Xing-zhu. Identification of robust blur parameter for uniform linear motion blurred images[J]. Editorial Office of Optics and Precision Engineering, 2013,21(9): 2430-2438
赵鹏曹军韦兴竹. 匀速直线运动模糊图像的模糊参数鲁棒识别[J]. 光学精密工程, 2013,21(9): 2430-2438 DOI： 10.3788/OPE.20132109.2430.

ZHAO Peng CAO Jun WEI Xing-zhu. Identification of robust blur parameter for uniform linear motion blurred images[J]. Editorial Office of Optics and Precision Engineering, 2013,21(9): 2430-2438 DOI： 10.3788/OPE.20132109.2430.

摘要

针对摄像机成像时经常产生匀速直线运动模糊，导致图像退化的现象，提出了恢复图像涉及的精确辨识运动模糊尺度的方法。由于利用Radon变换可以精确地识别运动模糊方向，通过图像旋转可以将运动模糊的方向旋转到水平轴，因此，只对水平方向的运动模糊图像进行研究。对于不带噪声的运动模糊图像，对其进行Fourier变换转化到频率域，使用BP神经网络检测运动模糊尺度；BP神经网络的输入量为频谱图中央区域的幅度加和。对于带噪声的运动模糊图像，先对其进行双谱变换，再使用BP神经网络检测运动模糊尺度；双谱中每列的最大值为BP神经网络的输入量。最后通过仿真实验验证了本文方法的正确性和有效性。验证结果显示，当噪声图像的信噪比SNR23 dB时，本文方法的模糊参数辨识平均误差5%，优于传统的运动模糊参数辨识方法。

Abstract

Uniform linear motion blur often occurs in the camera imaging，which results in image degradation seriously. Therefore

this paper proposes a method to identify the motion blur extent accurately. Since a motion blur angle could be identified accurately by Radon transform and the motion blur direction could be rotated to the horizontal

only the horizontal motion blurred images are needed to be researched. For a motion blurred image without noises

the Fourier transform is used to transform it to frequency domain

the motion blur length is estimated by the BP neural network and the input of BP network is the sum of the amplitudes in the central region of the spectrum. For a blurred image with noises

the bispectrum transform is applied

the motion blur length is estimated by BP neural network and the input of BP network is the maximum value of each column of bispectrum. The simulation experiments show that the proposed method in this paper is correct and efficient. When the SNR is larger or equal to 23 dB in noisy images

the mean error of blur parameter identification by proposed method is below 5%

which is superior to that of conventional schemes.

关键词

Keywords

references

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