周雪燕, 孔梦荣, 杨关. 多尺度纹理图像数据抗干扰信息映射方法研究[J]. 微电子学与计算机, 2017, 34(7): 128-131,136.
引用本文: 周雪燕, 孔梦荣, 杨关. 多尺度纹理图像数据抗干扰信息映射方法研究[J]. 微电子学与计算机, 2017, 34(7): 128-131,136.
ZHOU Xue-yan, KONG Meng-rong, YANG Guan. Research on Multi Scale Texture Image Data Anti Interference Information Mapping Method[J]. Microelectronics & Computer, 2017, 34(7): 128-131,136.
Citation: ZHOU Xue-yan, KONG Meng-rong, YANG Guan. Research on Multi Scale Texture Image Data Anti Interference Information Mapping Method[J]. Microelectronics & Computer, 2017, 34(7): 128-131,136.

多尺度纹理图像数据抗干扰信息映射方法研究

Research on Multi Scale Texture Image Data Anti Interference Information Mapping Method

  • 摘要: 针对当前的抗干扰信息融合方法受到噪点干扰的影响较大, 存在映射精度低、误差大的问题, 提出基于白平衡偏差补偿和小波尺度分解的多尺度纹理图像数据抗干扰信息映射方法.首先进行多尺度纹理图像数据的特征采集, 对采集的原始图像数据采用小波降噪方法进行提纯预处理, 然后进行图像的白平衡偏差补偿, 实现图像数据的修正和多尺度纹理信息的融合, 采用小波尺度分解方法进行图像数据抗干扰信息的特征提取和分层映射, 提高图像数据信息的融合深度.最后进行仿真测试, 结果表明, 采用该方法进行多尺度纹理图像数据抗干扰信息映射, 能提高图像数据的提取精度, 图像数据的信噪比和归一化相关系数较大, 表明抗干扰信息映射融合的较好, 鲁棒性较强.

     

    Abstract: Because of the influence of interference information fusion method of current noise interference is larger, the problems of low precision, big error mapping, a white balance error compensation and wavelet scale decomposition of multiscale texture image data based on the interference information mapping method. The first characteristic of multiscale texture image data acquisition, the original image data acquisition using wavelet denoising method for purification pretreatment, white balance deviation compensation and image fusion, correction of image data and multi-scale texture information, using wavelet scale image data interference information feature extraction and hierarchical decomposition map method to improve the fusion depth image data. The simulation test results show that, using the method of information mapping anti-jamming multiscale texture image data, can improve the extraction accuracy of image data, image data of the SNR is very large and the normalized correlation coefficient, indicate that the anti interference mapping information fusion is good, strong robustness.

     

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