【作者】 徐月美；

【导师】 张虹；

【作者基本信息】 中国矿业大学 ， 地图制图学与地理信息工程， 2012， 博士

【摘要】 多聚焦图像融合的目的是提取源图像中的清晰区域，将这些清晰区域组成一个各处都清晰的融合图像。在此过程中，如何精确的提取源图像的清晰区域直接决定了多聚焦图像的融合质量。图像融合分为三个层次：像素级图像融合、特征级图像融合和决策级图像融合。本文针对像素级图像融合，采用Q-Shift双数复小波和非下采样Contourlet等多尺度变换对多聚焦图像融合进行研究，提出了相应的图像融合算法。本文在深入研究小波变换特性的基础上，对多聚焦图像进行Q-Shift双树复小波变换。根据光学系统成像的原理“多聚焦图像中清晰图像的高频分量远大于模糊图像的高频分量，而清晰图像的低频系数不小于模糊图像的低频系数”和高低频系数间的相关性，对变换后的高低频系数采用“选择取大”的融合规则，提出了低频子带采用“局部区域标准方差取大”和高频子带采用“模值绝对值和取大”的融合准则。实验结果表明，它能够有效的实现多聚焦图像之间的融合，较好的保留源图像的细节信息，避免了小波变换中的重影等现象。由于小波变换不是最稀疏的图像表示方法，因此本文深入研究了能够真正实现图像稀疏表示的Contourlet变换，对多聚焦图像进行非下采样的Contourlet变换，根据NSCT变换方法，提出低频子带采用“区域能量取大”和高频子带采用“绝对值和取大”的融合准则。仿真实验从使用不同的融合规则和不同的多分辨率分析方法这两个方面分析了提出算法的有效性，并且与前面提出的基于Q-Shift双树复小波变换的融合结果做比较，取得了更好的融合质量。本文将Q-Shift双树复小波变换和非下采样Contourlet变换应用到多聚焦的彩色图像融合中，研究了彩色图像的融合算法。对彩色图像使用IHS变换提取彩色图像中的亮度和色度分量，然后对亮度分量提出了基于Q-Shift双树复小波变换和基于非下采样Contourlet变换的多聚焦彩色图像融合方法。仿真实验通过主观观察和性能参数：信息熵、平均梯度、方差、相关系数和均方根误差等对融合图像进行评价，实验结果表明本文提出的算法不但减少了算法的计算量，而且降低了偏色或失真的不确定因素。该论文有图53幅，表10个，参考文献192篇。更多还原

【Abstract】 Multi-focus image fusion means that the clear regions of source images areextracted and the corresponding clear regions are integrated into a fusion image inwhich every region is clear. Therefore, how to accurately extract the clear regionsdirectly decides the fusion quality of the multi-focus image. In general, studyingmulti-focus image fusion may be from three levels: pixel-level, feature-level anddecision level. In the thesis the multi-focus image fusion are studied by usingmulti-scale transforms, such as Q-Shift Dual-Tree Complex Wavelet andNonsubsampled Contourlet, based on the pixel-level image fusion and some imagefusion algorithms are proposed.Firstly, the features of wavelet transform are studied and Q-Shift Dual-TreeComplex Wavelet Transform(Q-Shift DT-CWT) is used for multi-focus image fusions.According to the principle of optical imaging, which is “In multi-focus image, thehigh frequency coefficients of clear image are far greater than it of fuzzy image andthe low frequency coefficients of clear image are not less than it of fuzzy image”, andthe correlation between high and low frequency coefficients, the method of selectivitymaximum is used for image fusion. So the rule of local area standard deviationmaximum selectivity is used for the low frequency and the rule of module absolutevalue sum maximum selectivity is used for the high frequency. By the subjectiveobservation and the objective evaluation, the experimental results show that theproposed algorithm can effectively realize the multi-focus image fusion, well preservethe image detail information, avoid the artifacts caused by incorrect pixel selectivityin wavelet transform.Secondly, the contourlet transform which is able to realize image sparserepresentation is studied because wavelet transform is not the most sqarse imagerepresentation method and Nonsubsampled Contourlet Transform(NSCT) is used formulti-focus image fusions. According to the principle of NSCT, the rule of regionenergy maximum selectivity is used for the low frequency and the rule of absolutevalue sum maximum selectivity is used for the high frequency. From the differentfusion rules and the different multi-resolution analysis, it is proved that the proposedalgorighm is effective and feasible. By the subjective observation and the objectiveevaluation, the experimental results show that the proposed algorithm obtained betterfusion quality than the algorithm based on Q-Shift DT-CWT proposed in before. Thirdly, the color multi-focus image fusion is studied by Q-Shift DT-CWT andNSCT in IHS. After IHS transformation, the I, H and S are separated from the colorimage. The I component is corresponded to the gray image and it is irrelevant to thecolor information, so only the I component is fused with rules which are proposed inbefore. By the subjective observation and the performance parameters, such asEntropy, Average Gradient, Mutual Information and Root Mean Square Error, toevaluate the experiment, the results show that the studied algorithms not only reducedthe computational complexity, but also reduced the uncertain factors of the color biasor distortion.更多还原