基于多尺度分解的图像融合研究
摘要
作为信息融合方法之一图像融合广泛应用于多聚焦图像、遥感图像以及空间图像中。本文基于多尺度分解的图像融合方法展开研究,论文完成的主要工作如下：
(1)完成了图像融合的预处理,研究了图像去噪的方法以及小波阈值去噪的阈值选取的方法。在小波阈值去噪的基础上,提出了种改进的去噪方法,这种方法既保证了去噪函数的连续性以及软阈值去噪方法的优点,又保留了硬阈值去噪方法减少对图像的缩减的特性。通过实验分析了该方法的去噪效果。
(2)给出了小波变换图像融合的方法与步骤。在基于平移不变离散小波变换的基础上,针对高频区域,提出了一种基于区域能量差的自适应加权系数的融合策略。提出了一种将区域能量取大值和区域能量加权相结合的方法,自适应加权系数可由区域能量差所确定。通过实验分析了阈值T对图像评价指标的影响,实验结果表明了该方法的良好效果。
(3)对非下采样Contourlet分解进行了研究,给出了Contourlet变换和NSCT变换应用于图像融合的步骤。基于NSCT提出了一种改进的高低频融合方法,在低频区域采用了标准差取大的融合策略,在高频部分采用区域轮廓能量对比度的融合策略。高频系数部分的最终决策由高频系数和低频系数的共同决定。通过实验分析了该方法的良好效果。
(4)设计并实现了图像融合工具系统,开发了图像预处理模块、图像融合模块以及图像评价模块,实现了图像融合。
As one field of information fusion, image fusion has been extensively applied in multi-focus images, remote sensing images and space images. This paper focuses on the methods of image fusion based on multi-scale decomposition. The main contributions can be summarized as the follows:
(1) The pretreatment of image fusion is accomplished in the thesis. The image denoising methods and the threshold selection methods of wavelet denoising are studied. An improved denoising method is proposed based on the wavelet denoising. This method not only keeps the continuity of the denoising function and the advantages of the soft threshold denoising method, but also keeps the less image reduced characteristics of the hard threshold denoising method. The denoising effect is analyzed by the experiments.
(2) The method and the steps of wavelet transform image fusion are provided. On the basis of shift invariant discrete wavelet transform, a new rule of the adaptive weighting coefficients based on region energy difference is proposed in the high-frequency sub-bands. A combination of region energy maximum method and the weighting coefficients method is proposed in the paper. The adaptive weighting coefficients can be determined by region energy difference. The relationship between the threshold T and image evaluation is analyzed and the improved effectiveness of the new fusion rule is shown by the experimental results.
(3) The nonsubsampled contourlet transform (NSCT) is studied in the paper. The steps of image fusion based on countourlet and NSCT are provided. An improved image fusion rule of low-frequency and high-frequency sub-bands based on NSCT is proposed. On the basis of NSCT, the maximum method of standard deviation is applied in the low-frequency sub-band, and the region contour energy contrast method is applied in the high-frequency sub-bands. The final decision of the high-frequency sub-bands are the joint decision of the low-frequency sub-band and high-frequency sub-bands. The improved effectiveness is analyzed by the experiments.
(4) The image fusion tools are designed and implemented. Pretreatment module, image fusion module and image evaluation module are developed and image fusion is realized.
(1)完成了图像融合的预处理,研究了图像去噪的方法以及小波阈值去噪的阈值选取的方法。在小波阈值去噪的基础上,提出了种改进的去噪方法,这种方法既保证了去噪函数的连续性以及软阈值去噪方法的优点,又保留了硬阈值去噪方法减少对图像的缩减的特性。通过实验分析了该方法的去噪效果。
(2)给出了小波变换图像融合的方法与步骤。在基于平移不变离散小波变换的基础上,针对高频区域,提出了一种基于区域能量差的自适应加权系数的融合策略。提出了一种将区域能量取大值和区域能量加权相结合的方法,自适应加权系数可由区域能量差所确定。通过实验分析了阈值T对图像评价指标的影响,实验结果表明了该方法的良好效果。
(3)对非下采样Contourlet分解进行了研究,给出了Contourlet变换和NSCT变换应用于图像融合的步骤。基于NSCT提出了一种改进的高低频融合方法,在低频区域采用了标准差取大的融合策略,在高频部分采用区域轮廓能量对比度的融合策略。高频系数部分的最终决策由高频系数和低频系数的共同决定。通过实验分析了该方法的良好效果。
(4)设计并实现了图像融合工具系统,开发了图像预处理模块、图像融合模块以及图像评价模块,实现了图像融合。
As one field of information fusion, image fusion has been extensively applied in multi-focus images, remote sensing images and space images. This paper focuses on the methods of image fusion based on multi-scale decomposition. The main contributions can be summarized as the follows:
(1) The pretreatment of image fusion is accomplished in the thesis. The image denoising methods and the threshold selection methods of wavelet denoising are studied. An improved denoising method is proposed based on the wavelet denoising. This method not only keeps the continuity of the denoising function and the advantages of the soft threshold denoising method, but also keeps the less image reduced characteristics of the hard threshold denoising method. The denoising effect is analyzed by the experiments.
(2) The method and the steps of wavelet transform image fusion are provided. On the basis of shift invariant discrete wavelet transform, a new rule of the adaptive weighting coefficients based on region energy difference is proposed in the high-frequency sub-bands. A combination of region energy maximum method and the weighting coefficients method is proposed in the paper. The adaptive weighting coefficients can be determined by region energy difference. The relationship between the threshold T and image evaluation is analyzed and the improved effectiveness of the new fusion rule is shown by the experimental results.
(3) The nonsubsampled contourlet transform (NSCT) is studied in the paper. The steps of image fusion based on countourlet and NSCT are provided. An improved image fusion rule of low-frequency and high-frequency sub-bands based on NSCT is proposed. On the basis of NSCT, the maximum method of standard deviation is applied in the low-frequency sub-band, and the region contour energy contrast method is applied in the high-frequency sub-bands. The final decision of the high-frequency sub-bands are the joint decision of the low-frequency sub-band and high-frequency sub-bands. The improved effectiveness is analyzed by the experiments.
(4) The image fusion tools are designed and implemented. Pretreatment module, image fusion module and image evaluation module are developed and image fusion is realized.
引文
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[2]刘斌,彭嘉雄.基于四通道不可分加性小波的多光谱图像融合[J].计算机学报,2009,32(2)：350-356.
[3]叶传奇.基于多尺度分解的多传感器图像融合算法研究[D].西安：西安电子科技大学博士学位论文,2009.
[4]朱四荣,王迎春.基于多小波变换的多聚焦图像融合[J].计算机工程与应用,2010,46(6)：169-170,226.
[5]陈浩,王延杰.基于拉普拉斯金字塔变换的图像融合算法研究[J].激光与红外,2009,39(4)：439-442.
[6]窦闻,陈云浩,何辉明等.光学遥感影像像素级融合的理论框架[J].测绘学报,2009,38(2)：131-137.
[7]Nianlong Han, Jinxing Hu, Wei Zhang. Multi-spectral and SAR images fusion via Mallat and A trous wavelet transform[C]. Geoinformatics,2010 18th International Conference,2010:1-4
[8]Wang, A., Haijing Sun, Yueyang Guan. The Application of Wavelet Transform to Multi-modality Medical Image Fusion[C].Networking, Sensing and Control,2006. ICNSC'06. Proceedings of the 2006 IEEE International Conference,2006:270-274
[9]胡俊峰,唐彩银,巩萍等.基于小波变换的CT/SPECT图像融合最佳层数选取[J].中国医疗设备,2009,24(3)：10-12.
[10]窦闻,孙洪泉,陈云浩等.基于光谱响应函数的遥感图像融合对比研究[J].光谱学与光谱分析,2011,31(3)：746-752.
[11]屈小波,闫敬文,杨贵德等.改进拉普拉斯能量和的尖锐频率局部化[J].光学精密工程,2009,17(5)：1203-1212.
[12]杨立才,刘延梅,刘欣等.基于小波包变换的医学图像融合方法[J].中国生物医学工程学报,2009,28(1)：12-16.
[13]王卫星,曾基兵.冗余提升不可分离小波的图像融合方法[J].电子科技大学学报,2009,38(1)：13-16.
[14]刘坤,郭雷,陈敬松等.基于区域分割的序列红外图像融合算法[J].红外与激光工程,2009,38(3)：553-558.
[15]杨风暴,蔺素珍,冷敏等.双色中波红外图像的分割支持度变换融合[J].红外与毫米波学报,2010,29(5)：362-366.
[16]常威威,郭雷,付朝阳等.利用脉冲耦合神经网络的高光谱多波段图像融合方法[J].红外与毫米波学报,2010,29(3)：205-209,235.
[17]廖家平,陶靖琦,赵熙临等.针对证据冲突状态的图像融合技术[J].华中科技大学学报：自然科学版,2012,40(3)：5-8.
[18]罗晓清,吴小俊.一种基于区域相似性的图像融合评价方法[J].电子学报,2010,38(5)：1152-1155.
[19]武治国,王延杰,李桂菊等.应用小波变换的自适应脉冲耦合神经网络在图像融合中的应用[J].光学精密工程,2010,18(3)：708-715.
[20]刘斌,祝青,彭嘉雄等.基于Red-Black小波变换的多光谱图像融合方法[J].仪器仪表学报,2011,32(2)：408-414.
[21]夏开建,姚宇峰,钟珊等.基于形态学小波变换的图像融合算法[J].计算机工程,2010,36(19)：224-226.
[22]Rajkumar, S., Kavitha, S.Redundancy. Discrete Wavelet Transform and Contourlet Transform for Multimodality Medical Image Fusion with Quantitative Analysis[C]. Emerging Trends in Engineering and Technology (ICETET),2010 3rd International Conference,2010:134-139.
[23]陈浩,王延杰.基于小波变换的图像融合技术研究[J].微电子学与计算机,2010,27(5)：39-41.
[24]刘凌湘,陈武凡,周凌宏等.基于小波变换的CT/PET图像融合最佳参数研究[J].中国生物医学工程学报,2010,29(4)：498-503.
[25]郭雷,程塨,赵天云等.基于小波变换和邻域特征的多聚焦图像融合算法[J].西北工业大学学报,2011,29(3)：454-459.
[26]王晓文,赵宗贵,汤磊等.一种新的红外与可见光图像融合评价方法[J].系统工程与电子技术,2012,34(5)：871-875.
[27]李亚春,武金岗,王净等.小波变换图像融合规则性能的分析研究[J].计算机工程与应用,2010,46(8)：180-182,189.
[28]王知强.一种基于新阈值函数的小波图像去噪算法[J].哈尔滨理工大学学报,20 11,1 6(4)：56-58.
[29]刘慧,彭良玉,刘美华等.基于小波变换的图像去噪研究[J].微型机与应用,2011,30(23)：54-55,59.
[30]李财莲,李志先,孙即祥等.一种新的小波收缩统一阈值函数[J].国防科技大学学报,2012,34(1)：155-159.
[3 1]周琳,方强,杨绍华等.基于小波变换的图像去噪方法研究[J].科技信息,2011,(32)：22-23.
[32]郑思莉.基于不同小波阈值函数的图像去噪[J].软件导刊,2012,11(5)：167-169.
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