计算机工程与应用 ›› 2011, Vol. 47 ›› Issue (31): 179-181.
• 图形、图像、模式识别 • 上一篇 下一篇
张利强，李 毅
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ZHANG Liqiang，LI Yi
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摘要： 为了能更好地进行多传感器图像融合，提出了一种基于双层并行PCNN和粗集理论的图像融合方法。该方法首先对两幅图像去噪，将一幅图像作为主PCNN网络的输入，另一幅图像作为从PCNN网络的输入，计算每幅图像的清晰度，分别将每幅图像的清晰度矩阵送入主从PCNN网络处理，然后根据粗集理论对原始图像分类，最后生成融合图像。该方法不仅能保留原图像信息，而且得到的融合图像清晰度高、对比度大。仿真实验结果以及与其他融合算法的比较，表明该算法的有效性和优越性。
关键词: 图像融合, 双层并行PCNN, 粗集理论, 清晰度, 主PCNN, 从PCNN
Abstract: In order to better carry out multi-sensor image fusion，a novel algorithm based on double Parallel Pulse Coupled Neural Network（PCNN） and rough set for image fusion is proposed.In the method，two original images are denoised firstly，one of the images is chosen randomly as the input to the main PCNN network，and the other original image as the input to the subsidiary one.The clarities of the two original images are calculated，and the two matrixs of the clarity are processed by the main and subsidiary PCNN network seperatedly.Then the original image pixels are classified based on rough set theory.Finally，a fusion image is created according to the classified results.This method not only retains the information of original images，and the obtaining image has larger clarity and higher contrast.Simulation results and comparison with other fusion algorithms show the effectiveness and superiority of the method.
Key words: image fusion, double Parallel Pulse Coupled Neural Network（PCNN）, rough set theory, clarity, main PCNN, subsidiary PCNN
张利强，李 毅. 基于双层并行PCNN和粗集理论的图像融合[J]. 计算机工程与应用, 2011, 47(31): 179-181.
ZHANG Liqiang，LI Yi. New image fusion algorithm based on PCNN and rough set[J]. Computer Engineering and Applications, 2011, 47(31): 179-181.
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http://cea.ceaj.org/CN/Y2011/V47/I31/179
