基于多尺度滤波和稀疏表示的图像融合方法研究
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摘要
图像融合是图像处理和计算机视觉领域多传感器图像信息综合利用的重要手段，它将同类或异类成像传感器获得的同一场景或目标的多幅图像进行综合处理，得到一幅包含源图像重要信息的合成图像，通过综合利用不同源图像之间的互补信息和冗余信息来获得对同一场景或目标更为准确、全面、可靠的描述。目前，图像融合技术已经在军事、医学疾病诊断、遥感等领域得到广泛应用。本文以经典的多尺度理论，以及最新的稀疏表示和低秩表示理论为基础，结合图像融合的特点，提出了多模图像融合方法和遥感图像融合方法。取得的主要研究成果如下：
首先，提出了一种基于多尺度联合滤波的遥感图像融合方法。多尺度方法由于具有良好的多尺度特性已经被广泛应用于遥感图像融合，它提取全色图像的细节，然后注入到低分辨率多光谱中去，从而提高图像的分辨率。然而由于不同波段的光谱响应特性不一样，导致全色图像与多光谱图像的细节不一样，传统多尺度方法在提取全色图像细节时忽略了这一点。针对这个问题，本文利用双重双边滤波对全色图像与多光谱图像进行联合滤波，从而在提取全色图像细节时考虑多光谱图像的特点，使得提取的细节更符合多光谱图像。为了从不同尺度全面提取全色图像的细节，将原来的双重双边滤波扩展到多尺度域，提出了多尺度联合滤波方法。此外，考虑到平移不变性对图像融合的重要性，采用à trous策略进行多尺度扩展来进一步提高遥感图像融合性能。
其次，提出了多尺度方向双边滤波方法及相应的多模图像融合方法。多尺度方法也被广泛用于多模图像融合，考虑到边缘和方向特征广泛存在于各种图像中，将边缘保持特性和方向捕获特性同时结合在多尺度方法中，提出了多尺度方向双边滤波方法。首先利用具有边缘保持特性的多尺度双边滤波对源图像进行多尺度分解获得多个高频子带和一个低频子带，再在高频子带上运用方向滤波来捕获方向特征，从而实现边缘保持和方向捕获。在此基础上，将提出的多尺度方向双边滤波方法应用于多模图像融合，首先对源图像进行多尺度方向双边滤波来获得各尺度子带，再根据一定的融合规则将不同源图像的子带进行融合，最后在融合子带上进行多尺度方向双边滤波的逆变换获得融合图像。在红外与可见光多模图像、医学多模图像上的融合实验验证了提出方法的有效性。
然后，提出了一种基于稀疏表示的遥感图像融合方法。稀疏表示是近年来图像处理领域的研究热点，是一种新型图像信息表示理论，它在过完备字典上以一种简洁的方式对图像进行表示获得稀疏系数，这些稀疏系数和对应的原子能够揭示图像的内在结构，初级视皮层神经元对来自视网膜的图像响应采用的就是稀疏表示的方式。本文结合稀疏表示和广义IHS变换，提出了一种新的遥感图像融合方法。人眼视觉系统不易感知红绿蓝等波段信息，而对亮度，色度，饱和度等信息敏感，考虑到这一特点，利用广义IHS变换获得多光谱图像的亮度分量。同时结合稀疏表示能够揭示图像内在结构的特点，利用稀疏表示对亮度分量和全色图像进行融合获得新的亮度分量。因不同光谱波段的细节是不一样的，若直接进行广义IHS逆变换会使得各波段添加的细节一样，为此，本文根据各波段的灰度强度来自适应地调整细节的添加量以更好地在提高空间分辨率时保持光谱特性。
最后，提出了结合非局部处理和低秩表示的多模图像融合方法。图像融合的目的是综合集成源图像的显著信息，图像像素或区域的显著性不仅与局部信息有关，还与非局部信息有关，而且非局部信息可以帮助判断像素或区域的显著性。而传统的图像融合方法只利用了图像的局部信息，忽略了非局部这一重要信息，使得显著信息判断失误，从而导致融合图像部分细节丢失，有时甚至在融合图像中出现扭曲。为了克服该问题，本文提出了一种结合非局部算子和低秩表示的多模图像融合方法。首先，对于源图像中一个给定的图像块，在非局部范围内寻找它的相似图像块，再对这些图像块以低秩的方式进行表示，低秩能够约束相似图像块的一致性，从而能够更加有效准确地综合集成源图像的显著信息。
Image fusion is an important way of integrating multisensor image informationin the field of image processing and computer vision. It is the process of combininginformation from two or multiple images of the same scene or object into a singlecomposite image, which is more informative and is more suitable for visualperception and computer processing. At present, image fusion has been widely used inthe field of military, medical diagnosis, remote sensing, etc. Based on the classicalmultiscale theory, the recent sparse representation and low rank representation theory,multimodal image fusion methods and remote sensing image fusion methods areproposed in this paper. The main contributions of this thesis are as follows.
Firstly, a remote sensing image fusion method based on multiscale joint filter isproposed. Multiscale methods have been widely used in remote sensing image fusion.Multiscale methods are used to extract details of panchromatic images, which areinjected into low resolution multispectral images to increase its resolution. Thespectral response of different bands is different, which results in that the details ofpanchromatic images are different from that of multispectral images. Traditionalmultiscale methods to extract details ignore this problem. This thesis uses dualbilateral filter to simultaneously consider the characteristics of panchromatic imagesand multispectral images. To extract multiscale details, multiscale dual bilateral filteris proposed. Considering that translation invariance is important for image fusion, theà trous scheme is used in the multiscale dual bilateral filter to ensure translationinvariance.
Secondly, the multiscale directional bilateral filter is developed and is applied tomultimodal image fusion. We know that edge and directional features are verycommon in various images, which means that edge and directional features are veryimportant for image processing. The multiscale directional bilateral filter is developedto combine edge preserving and direction capturing. The multiscale bilateral filterwith the ability of edge preserving is firstly applied to source images to obtain a lowfrequency subband and several high frequency subbands. Then, directional filter isapplied to high frequency subband to capture directional feature. The multiscaledirectional bilateral filter is used for multimodal image fusion to verify itseffectiveness. Source images are decomposed to a low frequency subband and several directional subbands by the multiscale directional bilateral fitler. According to a givenfusion rule, these subbands of different source images are fused. The inversemultiscale directional bilateral filter is applied to fused subbands to obtain the fusedimage. The experiments over infrared-visible multimodal images and medicalmultimodal images indicate the effectiveness of the proposed method.
Thirdly, a remote sensing image fusion method based on sparse representation isproposed. Sparse representation is a novel image representation theory, which cansparsely represent an image on overcomplete dictionary to obtain sparse coefficients.These coefficients and their corresponding atoms reveal the intrinsic properties ofimages effectively. Primary visual cortex neurons process the images from retina inthe same way of sparse coding. Combining sparse representation and generalized IHStransform, a novel remote sensing image fusion method is proposed in this thesis.Human visual system is insensitive to red, green and blue band information, and issensitive to intensity, hue, saturation information. Considering this characteristic, thegeneralized IHS transform is used to obtain intensity component of multispectralimages. The intensity component and panchromatic images are merged by sparserepresentation to obtain the fused intensity component. The details of differentspectral bands are different, and the direct inverse generalized IHS transform willresult in that the details added to each band are the same. To better improve spatialresolution and preserve spectral fidelity, the amount of details added to each band isadjusted according to their pixel value.
Finally, a multimodal image fusion method is proposed through combiningnonlocal operator and low rank representation. The aim of image fusion is to integratethe salient information of source images. The salience depends on not only localinformation but also nonlocal information. Traditional image fusion methods only uselocal information and neglect nonlocal information, which result in that some detailsin the fused image are lost. Thus, a new multimodal image fusion method combiningnonlocal operator and low rank representation is proposed in this thesis. For an imagepatch in the source image, its similar patches are searched in the nonlocal domain.These similar patches are represented in the way of low rank, and low rank canimpose the consistency among similar patches. Therefore, the proposed method caneffectively integrate the salient information of source images.
首先，提出了一种基于多尺度联合滤波的遥感图像融合方法。多尺度方法由于具有良好的多尺度特性已经被广泛应用于遥感图像融合，它提取全色图像的细节，然后注入到低分辨率多光谱中去，从而提高图像的分辨率。然而由于不同波段的光谱响应特性不一样，导致全色图像与多光谱图像的细节不一样，传统多尺度方法在提取全色图像细节时忽略了这一点。针对这个问题，本文利用双重双边滤波对全色图像与多光谱图像进行联合滤波，从而在提取全色图像细节时考虑多光谱图像的特点，使得提取的细节更符合多光谱图像。为了从不同尺度全面提取全色图像的细节，将原来的双重双边滤波扩展到多尺度域，提出了多尺度联合滤波方法。此外，考虑到平移不变性对图像融合的重要性，采用à trous策略进行多尺度扩展来进一步提高遥感图像融合性能。
其次，提出了多尺度方向双边滤波方法及相应的多模图像融合方法。多尺度方法也被广泛用于多模图像融合，考虑到边缘和方向特征广泛存在于各种图像中，将边缘保持特性和方向捕获特性同时结合在多尺度方法中，提出了多尺度方向双边滤波方法。首先利用具有边缘保持特性的多尺度双边滤波对源图像进行多尺度分解获得多个高频子带和一个低频子带，再在高频子带上运用方向滤波来捕获方向特征，从而实现边缘保持和方向捕获。在此基础上，将提出的多尺度方向双边滤波方法应用于多模图像融合，首先对源图像进行多尺度方向双边滤波来获得各尺度子带，再根据一定的融合规则将不同源图像的子带进行融合，最后在融合子带上进行多尺度方向双边滤波的逆变换获得融合图像。在红外与可见光多模图像、医学多模图像上的融合实验验证了提出方法的有效性。
然后，提出了一种基于稀疏表示的遥感图像融合方法。稀疏表示是近年来图像处理领域的研究热点，是一种新型图像信息表示理论，它在过完备字典上以一种简洁的方式对图像进行表示获得稀疏系数，这些稀疏系数和对应的原子能够揭示图像的内在结构，初级视皮层神经元对来自视网膜的图像响应采用的就是稀疏表示的方式。本文结合稀疏表示和广义IHS变换，提出了一种新的遥感图像融合方法。人眼视觉系统不易感知红绿蓝等波段信息，而对亮度，色度，饱和度等信息敏感，考虑到这一特点，利用广义IHS变换获得多光谱图像的亮度分量。同时结合稀疏表示能够揭示图像内在结构的特点，利用稀疏表示对亮度分量和全色图像进行融合获得新的亮度分量。因不同光谱波段的细节是不一样的，若直接进行广义IHS逆变换会使得各波段添加的细节一样，为此，本文根据各波段的灰度强度来自适应地调整细节的添加量以更好地在提高空间分辨率时保持光谱特性。
最后，提出了结合非局部处理和低秩表示的多模图像融合方法。图像融合的目的是综合集成源图像的显著信息，图像像素或区域的显著性不仅与局部信息有关，还与非局部信息有关，而且非局部信息可以帮助判断像素或区域的显著性。而传统的图像融合方法只利用了图像的局部信息，忽略了非局部这一重要信息，使得显著信息判断失误，从而导致融合图像部分细节丢失，有时甚至在融合图像中出现扭曲。为了克服该问题，本文提出了一种结合非局部算子和低秩表示的多模图像融合方法。首先，对于源图像中一个给定的图像块，在非局部范围内寻找它的相似图像块，再对这些图像块以低秩的方式进行表示，低秩能够约束相似图像块的一致性，从而能够更加有效准确地综合集成源图像的显著信息。
Image fusion is an important way of integrating multisensor image informationin the field of image processing and computer vision. It is the process of combininginformation from two or multiple images of the same scene or object into a singlecomposite image, which is more informative and is more suitable for visualperception and computer processing. At present, image fusion has been widely used inthe field of military, medical diagnosis, remote sensing, etc. Based on the classicalmultiscale theory, the recent sparse representation and low rank representation theory,multimodal image fusion methods and remote sensing image fusion methods areproposed in this paper. The main contributions of this thesis are as follows.
Firstly, a remote sensing image fusion method based on multiscale joint filter isproposed. Multiscale methods have been widely used in remote sensing image fusion.Multiscale methods are used to extract details of panchromatic images, which areinjected into low resolution multispectral images to increase its resolution. Thespectral response of different bands is different, which results in that the details ofpanchromatic images are different from that of multispectral images. Traditionalmultiscale methods to extract details ignore this problem. This thesis uses dualbilateral filter to simultaneously consider the characteristics of panchromatic imagesand multispectral images. To extract multiscale details, multiscale dual bilateral filteris proposed. Considering that translation invariance is important for image fusion, theà trous scheme is used in the multiscale dual bilateral filter to ensure translationinvariance.
Secondly, the multiscale directional bilateral filter is developed and is applied tomultimodal image fusion. We know that edge and directional features are verycommon in various images, which means that edge and directional features are veryimportant for image processing. The multiscale directional bilateral filter is developedto combine edge preserving and direction capturing. The multiscale bilateral filterwith the ability of edge preserving is firstly applied to source images to obtain a lowfrequency subband and several high frequency subbands. Then, directional filter isapplied to high frequency subband to capture directional feature. The multiscaledirectional bilateral filter is used for multimodal image fusion to verify itseffectiveness. Source images are decomposed to a low frequency subband and several directional subbands by the multiscale directional bilateral fitler. According to a givenfusion rule, these subbands of different source images are fused. The inversemultiscale directional bilateral filter is applied to fused subbands to obtain the fusedimage. The experiments over infrared-visible multimodal images and medicalmultimodal images indicate the effectiveness of the proposed method.
Thirdly, a remote sensing image fusion method based on sparse representation isproposed. Sparse representation is a novel image representation theory, which cansparsely represent an image on overcomplete dictionary to obtain sparse coefficients.These coefficients and their corresponding atoms reveal the intrinsic properties ofimages effectively. Primary visual cortex neurons process the images from retina inthe same way of sparse coding. Combining sparse representation and generalized IHStransform, a novel remote sensing image fusion method is proposed in this thesis.Human visual system is insensitive to red, green and blue band information, and issensitive to intensity, hue, saturation information. Considering this characteristic, thegeneralized IHS transform is used to obtain intensity component of multispectralimages. The intensity component and panchromatic images are merged by sparserepresentation to obtain the fused intensity component. The details of differentspectral bands are different, and the direct inverse generalized IHS transform willresult in that the details added to each band are the same. To better improve spatialresolution and preserve spectral fidelity, the amount of details added to each band isadjusted according to their pixel value.
Finally, a multimodal image fusion method is proposed through combiningnonlocal operator and low rank representation. The aim of image fusion is to integratethe salient information of source images. The salience depends on not only localinformation but also nonlocal information. Traditional image fusion methods only uselocal information and neglect nonlocal information, which result in that some detailsin the fused image are lost. Thus, a new multimodal image fusion method combiningnonlocal operator and low rank representation is proposed in this thesis. For an imagepatch in the source image, its similar patches are searched in the nonlocal domain.These similar patches are represented in the way of low rank, and low rank canimpose the consistency among similar patches. Therefore, the proposed method caneffectively integrate the salient information of source images.
引文
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