像素级图像融合及其关键技术研究
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摘要
图像融合是将多个相同或不同类型的成像传感器获取的同一场景的多幅图像信息加以综合与提取,从而产生比任何单一图像信息对景物更加精确的描述。图像融合一般可分为像素级、特征级和决策级图像融合。本文针对像素级图像融合技术中需要解决的关键问题,重点研究了其中的三项关键技术:像素级图像融合预处理中的图像降噪技术、多聚焦图像融合技术以及全色与多光谱遥感影像融合技术。主要内容为:
1.提出了一种基于人类视觉系统的图像去噪方法。该方法结合了像素分类与小波变换,在不同的图像区域采用不同的阈值进行去噪,可有效提高图像去噪的效果,同时较好的保持了图像细节。
2.提出了一种有利于图像压缩的小波图像去噪方法以及一种小波系数校验方法。该去噪方法利用图像小波系数的层内相关性进行图像去噪,并可与后续的图像压缩处理有效结合。
3.提出了一种基于局部区域梯度信息的多分辨率图像融合算法及其改进算法。改进算法对不同源图像的对应尺度系数进行自适应加权相加,以获得融合后的尺度系数。这两种方法的融合效果均优于常用融合方法。
4.提出了一种基于离散余弦变换以及一种结合小波变换与离散余弦变换的图像融合新方法。前者的计算量相对较少,适用于实时处理,而后者则能有效提高图像融合的质量。
5.提出了一种基于支持向量机与图像块分割的自适应图像融合策略。该方法依据多聚焦源图像块所在的位置,采用不同大小的图像块进行自适应融合处理,可有效提高图像的融合效果。
6.提出了一种结合块分割与多分辨率分析的多聚焦图像融合方法。该方法可与现有的基于多分辨率分析的多聚焦图像融合方法相结合,能有效提高这些方法的融合效果。
7.提出了一种基于离散余弦变换与IHS(Intensity-hue-saturation,IHS)变换的多光谱与全色遥感影像融合方法及其改进算法。这两种方法可直接在离散余弦变换域进行遥感影像融合,适合压缩格式的遥感影像快速融合。利用这两种方法的思想在空域结合基于IHS变换的融合方法,仅需较小的计算量,在提高融合图像空间分辨率的同时,保持了绿色植被区域的光谱特性。
8.提出了一种基于抽样小波变换与IHS变换的高空间分辨率遥感影像融合方法。该方法的计算量接近于基于抽样小波变换的常用融合方法,并可获得近似甚至优于冗余小波变换的融合效果。
上述各个技术研究点均进行了相应的计算机仿真与性能分析。本论文的所有研究工作在图像去噪与图像融合处理领域具有重要的理论与应用价值。
The image fusion means to integrate and synthesize the information of the source images for the same scene acquired with the same or different kinds of image sensors, and generate a single image which contains a more accurate description of the scene than any of the individual source images. The image fusion can be divided into three fusion level, namely: pixel, feature and decision levels. Among unsolved key problems of the pixel-level image fusion, we mainly study three key technologies, namely: the image denoising technology in the pixel-level image fusion preprocessing, the multi-focus image fusion technology, and the panchromatic and multispectral remote sensing image fusion technology. The main results are as follows:
1. An image denoising scheme based on human visual system is proposed. This method combines the pixel classification with the wavelet transform and denoises the different image areas with the different thresholds respectively. This approach reduces the image noise effectively and keeps the image details well.
2. An image denoising algorithm suitable for image compression and a wavelet coefficient verification method are proposed. This denoising method exploits the intrascale wavelet coefficients' correlation to denoise the image. This approach can be combined with the succedent image compression operation effectively.
3. A local gradient information based multiresolution image fusion algorithm and its modified method are proposed. The modified method uses adaptive weighted addition to obtain the fused scale coefficients according to the corresponding scale coefficients of the different source images. The fusion results of the proposed methods are superior to those of conventional fusion schemes.
4. An image fusion algorithm based on the discrete cosine transform (DCT) and a new image fusion scheme using the wavelet transform and DCT are proposed. The former method with the low computational cost is more suitable for the real-time processing, while the latter one can improve the fusion quality effectively.
5. An adaptive multi-focus image fusion algorithm based on the support vector machine (SVM) and image block segment is proposed. The original images are fused adaptively with different block sizes according to the positions of the original image blocks with the proposed scheme. This method can improve the fusion quality effectively.
6. An image fusion algorithm based on multi-resolution and image block segment is proposed. The proposed method can be combined with the existing multi-resolution based multi-focus image fusion algorithms and improves the fusion results of these methods.
7. A multispectral and panchromatic remote sensing image fusion algorithm using discrete cosine transform and Intensity-hue-saturation (IHS) transform and its modified approach are proposed. The proposed approaches can be performed in the DCT compression domain directly and are suitable for fast image fusion in the compression domain. The idea of the suggested methods combined with the traditional IHS-based image fusion scheme can be employed to improve the spatial quality of the fused image and keep the spectral characteristics of the green vegetation areas with the low computational complexity.
8. A remote sensing image fusion algorithm with high spatial quality based on IHS transform and the decimated wavelet transform is proposed. The computational cost of the proposed method is close to that of the conventional decimated wavelet transform based fusion approach. The fusion results of the proposed fusion scheme are similar and even superior to those of the traditional undecimated wavelet transform based fusion algorithm.
Computer simulation and its performance analysis are carried on with all techniques discussed above. All the research works in this dissertation have important values in the theory and application on image denoising and image fusion fields.
1.提出了一种基于人类视觉系统的图像去噪方法。该方法结合了像素分类与小波变换,在不同的图像区域采用不同的阈值进行去噪,可有效提高图像去噪的效果,同时较好的保持了图像细节。
2.提出了一种有利于图像压缩的小波图像去噪方法以及一种小波系数校验方法。该去噪方法利用图像小波系数的层内相关性进行图像去噪,并可与后续的图像压缩处理有效结合。
3.提出了一种基于局部区域梯度信息的多分辨率图像融合算法及其改进算法。改进算法对不同源图像的对应尺度系数进行自适应加权相加,以获得融合后的尺度系数。这两种方法的融合效果均优于常用融合方法。
4.提出了一种基于离散余弦变换以及一种结合小波变换与离散余弦变换的图像融合新方法。前者的计算量相对较少,适用于实时处理,而后者则能有效提高图像融合的质量。
5.提出了一种基于支持向量机与图像块分割的自适应图像融合策略。该方法依据多聚焦源图像块所在的位置,采用不同大小的图像块进行自适应融合处理,可有效提高图像的融合效果。
6.提出了一种结合块分割与多分辨率分析的多聚焦图像融合方法。该方法可与现有的基于多分辨率分析的多聚焦图像融合方法相结合,能有效提高这些方法的融合效果。
7.提出了一种基于离散余弦变换与IHS(Intensity-hue-saturation,IHS)变换的多光谱与全色遥感影像融合方法及其改进算法。这两种方法可直接在离散余弦变换域进行遥感影像融合,适合压缩格式的遥感影像快速融合。利用这两种方法的思想在空域结合基于IHS变换的融合方法,仅需较小的计算量,在提高融合图像空间分辨率的同时,保持了绿色植被区域的光谱特性。
8.提出了一种基于抽样小波变换与IHS变换的高空间分辨率遥感影像融合方法。该方法的计算量接近于基于抽样小波变换的常用融合方法,并可获得近似甚至优于冗余小波变换的融合效果。
上述各个技术研究点均进行了相应的计算机仿真与性能分析。本论文的所有研究工作在图像去噪与图像融合处理领域具有重要的理论与应用价值。
The image fusion means to integrate and synthesize the information of the source images for the same scene acquired with the same or different kinds of image sensors, and generate a single image which contains a more accurate description of the scene than any of the individual source images. The image fusion can be divided into three fusion level, namely: pixel, feature and decision levels. Among unsolved key problems of the pixel-level image fusion, we mainly study three key technologies, namely: the image denoising technology in the pixel-level image fusion preprocessing, the multi-focus image fusion technology, and the panchromatic and multispectral remote sensing image fusion technology. The main results are as follows:
1. An image denoising scheme based on human visual system is proposed. This method combines the pixel classification with the wavelet transform and denoises the different image areas with the different thresholds respectively. This approach reduces the image noise effectively and keeps the image details well.
2. An image denoising algorithm suitable for image compression and a wavelet coefficient verification method are proposed. This denoising method exploits the intrascale wavelet coefficients' correlation to denoise the image. This approach can be combined with the succedent image compression operation effectively.
3. A local gradient information based multiresolution image fusion algorithm and its modified method are proposed. The modified method uses adaptive weighted addition to obtain the fused scale coefficients according to the corresponding scale coefficients of the different source images. The fusion results of the proposed methods are superior to those of conventional fusion schemes.
4. An image fusion algorithm based on the discrete cosine transform (DCT) and a new image fusion scheme using the wavelet transform and DCT are proposed. The former method with the low computational cost is more suitable for the real-time processing, while the latter one can improve the fusion quality effectively.
5. An adaptive multi-focus image fusion algorithm based on the support vector machine (SVM) and image block segment is proposed. The original images are fused adaptively with different block sizes according to the positions of the original image blocks with the proposed scheme. This method can improve the fusion quality effectively.
6. An image fusion algorithm based on multi-resolution and image block segment is proposed. The proposed method can be combined with the existing multi-resolution based multi-focus image fusion algorithms and improves the fusion results of these methods.
7. A multispectral and panchromatic remote sensing image fusion algorithm using discrete cosine transform and Intensity-hue-saturation (IHS) transform and its modified approach are proposed. The proposed approaches can be performed in the DCT compression domain directly and are suitable for fast image fusion in the compression domain. The idea of the suggested methods combined with the traditional IHS-based image fusion scheme can be employed to improve the spatial quality of the fused image and keep the spectral characteristics of the green vegetation areas with the low computational complexity.
8. A remote sensing image fusion algorithm with high spatial quality based on IHS transform and the decimated wavelet transform is proposed. The computational cost of the proposed method is close to that of the conventional decimated wavelet transform based fusion approach. The fusion results of the proposed fusion scheme are similar and even superior to those of the traditional undecimated wavelet transform based fusion algorithm.
Computer simulation and its performance analysis are carried on with all techniques discussed above. All the research works in this dissertation have important values in the theory and application on image denoising and image fusion fields.
引文
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