计算机应用 ›› 2011, Vol. 31 ›› Issue (09): 2506-2508.DOI: 10.3724/SP.J.1087.2011.02506

• 图形图像技术 • 上一篇    下一篇

基于局部标准差和方向微分的模糊方向识别

范海菊,张爱丽,冯乃勤   

  1. 河南师范大学 计算机与信息技术学院,河南 新乡 453007
  • 收稿日期:2011-03-09 修回日期:2011-04-18 发布日期:2011-09-01 出版日期:2011-09-01
  • 通讯作者: 范海菊
  • 作者简介:范海菊(1979-),女,河南新乡人,讲师,硕士,主要研究方向:数字图像处理、建模与仿真;
    张爱丽(1966-),女,河南新乡人,副教授,主要研究方向:移动通信系统;
    冯乃勤(1953-),男,河南新乡人,教授,博士,主要研究方向:神经网络、人工智能。
  • 基金资助:
    河南省重点科技攻关项目(102102210180)

Blurred direction identification based on local standard deviation and directional derivation

FAN Hai-ju,ZHANG Ai-li,FENG Nai-qin   

  1. College of Computer and Information Science, Henan Normal University, Xinxiang Henan 453007, China
  • Received:2011-03-09 Revised:2011-04-18 Online:2011-09-01 Published:2011-09-01
  • Contact: FAN Hai-ju

摘要: 针对最小方向微分算法在运动模糊方向识别中误差大和实时性差的缺点,提出利用局部标准差和方向微分相结合的方法鉴定模糊方向。该方法首先对模糊图像进行局部标准差滤波以增强模糊方向上的纹理细节,然后利用双线性插值求最小方向微分和,其对应的方向即为模糊方向;在对方向微分和曲线归纳分析后找到了其内在规律,基于此规律提出范围对半递减搜索最小值的方法,减少了搜索次数。实验结果表明该算法识别精度高,抗干扰性强,实时性好。

关键词: 运动模糊, 模糊方向, 局部标准差, 方向微分, 双线性插值

Abstract: Concerning the shortcomings of big recognition error and bad real-time calculation by the minimum differential directional algorithm, a method in combination with local standard deviation and directional derivation was put forward to identify the blurred direction of motion blur image. Firstly, the motion blur image was filtered by local standard deviation to enhance texture details in blurred direction. Secondly, the minimum directional derivation summation was obtained by bilinear interpolation, and its corresponding direction was the blurred direction. Meanwhile, the inherent law was found after concluding and analyzing the minimum directional derivation summation curve. Based on this law, the method of search range decreasing in half was presented to search minimum, which could reduce searching times. The simulation results show that this algorithm not only has high precision and strong immunity, but also meets the requirement of real-time calculation.

Key words: motion blur, blurred direction, local standard deviation, directional derivation, bilinear interpolation

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