基于小波的像素级图像融合算法研究
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摘要
图像融合是将同一场景的多幅图像进行综合以得到关于该场景更加准确描述的信息处理过程。融合过程可以在不同的层次上进行,按照信息抽象程度从低到高为:信号级、像素级、特征级、符号级。像素级图像融合直接基于成像传感器获得的像素信息进行融合处理;融合结果为一幅图像,该图像通常更适合人类和机器感知,或更适合后续处理任务,如分割、特征提取或目标识别。目前绝大多数融合算法研究都集中在这一层次上。像素级图像融合在军事、遥感、医学成像、机器人、安全和监控等领域有着广泛的应用。经过二十多年的发展,像素级图像融合形成了一个以金字塔分解和小波变换方法为代表的一般性图像融合方案——基于多尺度分解的图像融合方案。
本论文研究了基于小波的像素级图像融合算法。研究围绕解决小波图像融合算法存在的移变性和冗余性问题展开,着重对融合算法的核心——小波多尺度分解方法进行探讨。主要研究成果如下:
1.提出了一种新的基于低冗余离散小波框架的图像融合算法。针对标准离散小波变换和无下采样离散小波变换在图像融合应用中的优缺点,提出滤波级数和重采样级数分离的概念,发展了一种扩展的小波变换模型,并证明这个模型在l2空间上的框架性;优化扩展模型的重采样策略,在确保较好移不变性的前提下,尽量降低分解系数中的冗余,得到低冗余离散小波框架;将低冗余离散小波框架应用到多尺度图像融合方案中;基于仿真和真实图像(序列)的融合实验表明基于这种框架的融合算法可以有效的克服标准小波算法存在的移变性问题,改善融合结果,同时又避免了无下采样小波算法在解决该问题时引入过大冗余从而导致计算量过大的问题。
2.提出了一种新的基于梅花形采样离散小波框架的图像融合算法。首先证明了多维完全重构滤波器组重采样格的可替换性,并给出了可替换条件;随后证明了利用该条件构建的冗余完全重构滤波器组构成l_2 ( Z~n)空间上的紧框架。利用以上结果对可分离二维离散小波变换的矩形重采样格进行替换,得到梅花形采样离散小波框架。这种小波框架具有近似的移不变性和较低的冗余度,并且具有中间尺度,频域分辨率更高;将其应用到多尺度融合方案中可以快速的获得高质量的融合结果。
3.提出了两种基于非线性小波的图像融合算法:基于无下采样形态学Haar小波变换和基于无下采样最大提升方案的图像融合算法。与常规线性小波变换相比,形态学Haar小波变换和最大提升方案在计算、像素信息提取和硬件实现等方面具有优势;但是由于下采样环节的存在,上述两种非线性小波变换不具备移不变性,在融合结果中会引入较严重的不一致信息。本文利用无下采样方法对两种变换进行移不变扩展;扩展后的变换应用到多尺度融合方案中得到了较好的融合效果,尤其对于医学图像融合和可见光-红外图像序列融合。
4.基于已有的和本文提出的融合算法,开发了一套可见光与红外动态图像融合系统。该系统可以完成对可见光和红外成像传感器输出图像序列的实时配准和同步采集,并能离线的进行多种算法的融合处理以及融合结果的定量评价。
Image fusion is the process by which multiple images of the same scene are combined to generate a more accurate description of the scene than any of the individual source images. Fusion process can be performed at different levels of information representation, sorted in ascending order of abstraction: signal, pixel, feature and symbol levels. Pixel-level image fusion refers to the process directly based on the pixel information from individual sensors; fusion result is an image, which usually more suitable for human and machine perception, or further image-processing tasks, such as segmentation, feature extraction and object recognition. Almost all image fusion algorithms developed to date fall into this category. Pixel-level image fusion has a wide range of applications in military, remote sensing, medical imaging, robots, security and surveillance, etc. After over twenty years of development, pixel-level image fusion forms a generic image fusion scheme—multiscale-decomposition-based image fusion scheme, represented by the pyramid and the wavelet methods.
In this dissertation, wavelet-based pixel-level image fusion algorithms are studied. The research focuses on solving the problems of shift-variance and redundancy in wavelet algorithms. The core of wavelet algorithms—wavelet-multiscale-decomposition is discussed emphatically. The main contributions of this work are summarized as follows:
1. A novel image fusion algorithm based on the low-redundancy discrete wavelet frame is proposed. The merits and demerits of the standard discrete wavelet transform and its undecimated version in image fusion are studied; following this, the conception of separating filtering level and resampling level is proposed, an extended model for wavelet transforms is derived, and the“frame”property of the model in l 2 space is proved. The resampling strategies of the model are optimized to decrease as possible the redundancy of decomposition coefficients under the condition of good shift invariance; as a result, the low-redundancy discrete wavelet frame is obtained. The frame is incorporated into the multiscale fusion scheme. The fusion experiments based on synthetic and real-world images (sequences) demonstrated that the fusion algorithm based on the frame does overcome the shift variance problem of the standard wavelet algorithm, improving the results of fusion; at the same time, it avoids the problem of excessive computational cost, which is caused by the undecimated wavelet algorithm in solving the same problem, due to overmuch redundancy.
2. A novel image fusion algorithm based on the quincunx-sampled discrete wavelet frame is proposed. Firstly, the replaceability of the resampling lattice for multi-dimensional perfect reconstruction filter banks is proved, and a replacing condition is given; then, it is shown that the redundant perfect reconstruction filter banks derived by the condition constitute tight frames in l_2( Z~n) space. Following the above results, the quincunx-sampled discrete wavelet frame is obtained by replacing the rectangular resampling lattice of the standard separable two-dimensional discrete wavelet transform. The frame provides near shift invariance as well as very low redundancy; in addition, it has intermediate scales, increasing the sampling in frequency. High-quality fusion results can be generated quickly using the frame in the multiscale fusion scheme.
3. Two nonlinear wavelet image fusion algorithms are proposed: the fusion algorithms based on the undecimated morphological Haar wavelet transform and based on the undecimated max-lifting scheme. The morphological Haar wavelet transform and the max-lifting scheme, compared with the linear wavelets, have advantages in terms of computation, pixel information extraction, hardware implementation, etc; however, two transforms lack shift invariance due to downsampling steps, resulting in severe disturbing information in fusion images. In this work, two transforms are extended for shift invariance using the undecimated method. The extended transforms are incorporated into the multiscale fusion scheme, producing inspiring results, especially in medical images fusion and visible light - infrared image sequences fusion.
4. A visible light and infrared dynamic image fusion system is developed based on the fusion algorithms, existing and new proposed in this work. The system provides real-time registration and synchronous capture (store) to the frames generated by visible-light and infrared imaging devices; it also provides off-line fusion processing to the captured frames using various algorithms, as well as assessment for the fusion results.
本论文研究了基于小波的像素级图像融合算法。研究围绕解决小波图像融合算法存在的移变性和冗余性问题展开,着重对融合算法的核心——小波多尺度分解方法进行探讨。主要研究成果如下:
1.提出了一种新的基于低冗余离散小波框架的图像融合算法。针对标准离散小波变换和无下采样离散小波变换在图像融合应用中的优缺点,提出滤波级数和重采样级数分离的概念,发展了一种扩展的小波变换模型,并证明这个模型在l2空间上的框架性;优化扩展模型的重采样策略,在确保较好移不变性的前提下,尽量降低分解系数中的冗余,得到低冗余离散小波框架;将低冗余离散小波框架应用到多尺度图像融合方案中;基于仿真和真实图像(序列)的融合实验表明基于这种框架的融合算法可以有效的克服标准小波算法存在的移变性问题,改善融合结果,同时又避免了无下采样小波算法在解决该问题时引入过大冗余从而导致计算量过大的问题。
2.提出了一种新的基于梅花形采样离散小波框架的图像融合算法。首先证明了多维完全重构滤波器组重采样格的可替换性,并给出了可替换条件;随后证明了利用该条件构建的冗余完全重构滤波器组构成l_2 ( Z~n)空间上的紧框架。利用以上结果对可分离二维离散小波变换的矩形重采样格进行替换,得到梅花形采样离散小波框架。这种小波框架具有近似的移不变性和较低的冗余度,并且具有中间尺度,频域分辨率更高;将其应用到多尺度融合方案中可以快速的获得高质量的融合结果。
3.提出了两种基于非线性小波的图像融合算法:基于无下采样形态学Haar小波变换和基于无下采样最大提升方案的图像融合算法。与常规线性小波变换相比,形态学Haar小波变换和最大提升方案在计算、像素信息提取和硬件实现等方面具有优势;但是由于下采样环节的存在,上述两种非线性小波变换不具备移不变性,在融合结果中会引入较严重的不一致信息。本文利用无下采样方法对两种变换进行移不变扩展;扩展后的变换应用到多尺度融合方案中得到了较好的融合效果,尤其对于医学图像融合和可见光-红外图像序列融合。
4.基于已有的和本文提出的融合算法,开发了一套可见光与红外动态图像融合系统。该系统可以完成对可见光和红外成像传感器输出图像序列的实时配准和同步采集,并能离线的进行多种算法的融合处理以及融合结果的定量评价。
Image fusion is the process by which multiple images of the same scene are combined to generate a more accurate description of the scene than any of the individual source images. Fusion process can be performed at different levels of information representation, sorted in ascending order of abstraction: signal, pixel, feature and symbol levels. Pixel-level image fusion refers to the process directly based on the pixel information from individual sensors; fusion result is an image, which usually more suitable for human and machine perception, or further image-processing tasks, such as segmentation, feature extraction and object recognition. Almost all image fusion algorithms developed to date fall into this category. Pixel-level image fusion has a wide range of applications in military, remote sensing, medical imaging, robots, security and surveillance, etc. After over twenty years of development, pixel-level image fusion forms a generic image fusion scheme—multiscale-decomposition-based image fusion scheme, represented by the pyramid and the wavelet methods.
In this dissertation, wavelet-based pixel-level image fusion algorithms are studied. The research focuses on solving the problems of shift-variance and redundancy in wavelet algorithms. The core of wavelet algorithms—wavelet-multiscale-decomposition is discussed emphatically. The main contributions of this work are summarized as follows:
1. A novel image fusion algorithm based on the low-redundancy discrete wavelet frame is proposed. The merits and demerits of the standard discrete wavelet transform and its undecimated version in image fusion are studied; following this, the conception of separating filtering level and resampling level is proposed, an extended model for wavelet transforms is derived, and the“frame”property of the model in l 2 space is proved. The resampling strategies of the model are optimized to decrease as possible the redundancy of decomposition coefficients under the condition of good shift invariance; as a result, the low-redundancy discrete wavelet frame is obtained. The frame is incorporated into the multiscale fusion scheme. The fusion experiments based on synthetic and real-world images (sequences) demonstrated that the fusion algorithm based on the frame does overcome the shift variance problem of the standard wavelet algorithm, improving the results of fusion; at the same time, it avoids the problem of excessive computational cost, which is caused by the undecimated wavelet algorithm in solving the same problem, due to overmuch redundancy.
2. A novel image fusion algorithm based on the quincunx-sampled discrete wavelet frame is proposed. Firstly, the replaceability of the resampling lattice for multi-dimensional perfect reconstruction filter banks is proved, and a replacing condition is given; then, it is shown that the redundant perfect reconstruction filter banks derived by the condition constitute tight frames in l_2( Z~n) space. Following the above results, the quincunx-sampled discrete wavelet frame is obtained by replacing the rectangular resampling lattice of the standard separable two-dimensional discrete wavelet transform. The frame provides near shift invariance as well as very low redundancy; in addition, it has intermediate scales, increasing the sampling in frequency. High-quality fusion results can be generated quickly using the frame in the multiscale fusion scheme.
3. Two nonlinear wavelet image fusion algorithms are proposed: the fusion algorithms based on the undecimated morphological Haar wavelet transform and based on the undecimated max-lifting scheme. The morphological Haar wavelet transform and the max-lifting scheme, compared with the linear wavelets, have advantages in terms of computation, pixel information extraction, hardware implementation, etc; however, two transforms lack shift invariance due to downsampling steps, resulting in severe disturbing information in fusion images. In this work, two transforms are extended for shift invariance using the undecimated method. The extended transforms are incorporated into the multiscale fusion scheme, producing inspiring results, especially in medical images fusion and visible light - infrared image sequences fusion.
4. A visible light and infrared dynamic image fusion system is developed based on the fusion algorithms, existing and new proposed in this work. The system provides real-time registration and synchronous capture (store) to the frames generated by visible-light and infrared imaging devices; it also provides off-line fusion processing to the captured frames using various algorithms, as well as assessment for the fusion results.
引文
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