基于图像分析的自然彩色夜视成像方法研究
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摘要
夜视技术借助于光电成像器件拓展了人类的光谱响应范围尤其是在黑暗中观察景物的能力,使夜晚变得透明。传统的夜视图像都是单色,不利于对于场景的理解及目标的识别。随着对色彩在人类认知系统中所起作用的认识逐步加深,如何实现彩色夜视、尤其是与人眼视觉特性相符合的自然彩色夜视,已经成为夜视领域的研究热点。自然彩色夜视技术可以使夜视图像获得最佳的观测性能,在各种军用及民用领域,如战场监控、情报传送、刑事侦察、安全检查、交通管制、夜间导航、文物保护等方面有着重要应用。
本论文首先从夜视图像的成像特性和应用特性出发,探讨具有景物深度及空间立体视觉感的自然彩色夜视实现的问题、单波段红外热像的自然彩色夜视实现的问题、红外/微光双波段图像的实时自然彩色夜视实现的问题；其次从与自然彩色夜视实现过程相关的数学模型出发,探讨用于图像识别的数据降维算法和用于模型训练的稀疏学习算法。具体而言,论文的主要研究内容和创新点如下：
(1)具有景物深度及空间立体视觉感的自然彩色夜视实现方法研究。
提出夜视图像应具有立体空间感的新问题。并基于夜视图像的特性,提出利用单目双波段夜视图像中的红外/微光的强度来估计景物的深度信息,再利用色彩饱和度变化来增强夜视图像空间感的方法。该方法首先通过构建一个夜视图像的模式数据库,利用辐射/反射特征和纹理特征来识别图像中的景物类别、并为各景物赋予其相应的特征色彩,再根据估计的深度信息对景物色彩的饱和度进行调制,以便获得符合人眼观察规律的具有空间立体视觉感的彩色夜视图像视觉。最后通过实验证明这种彩色夜视图像不仅具有与人眼视觉特性相吻合的自然色彩,更具有与景物深度相吻合的空间立体视觉感,从而可以显著改善观察者对夜视图像的理解效果、提高目标识别率。
(2)单波段红外热像的自然彩色夜视实现方法研究。
提出基于单波段红外热像实现自然彩色夜视的新问题。为克服红外图像缺乏细节、局部信息不足的问题,提出使用一种具有“多尺度”和“空间上下文”信息的特征向量来对像素点进行分析；为解决红外图像的亮度分布规律与可见光图像极为不同的问题,首次采用“监督学习”的方法来建立色彩估计模型,给出一种基于线性回归的线性模型和一种基于支持向量回归(SVR)的非线性模型。该方法不同于以往的基于“多波段图像融合技术”的彩色夜视实现方法,可直接基于单波段红外热像实现彩色夜视,在提升彩色夜视系统便携性、降低成本等方面具有重大价值。最后通过实验证明该方法可以有效地实现单波段红外热像的自然彩色夜视。
(3)红外/微光双波段图像的实时自然彩色夜视实现方法研究。
为了克服以往的实时彩色夜视实现方法不适用于红外/微光双波段图像的问题,根据红外/微光双波段图像的特性设计了一种新的“自然—高亮色彩查找表”。不同于以前的方法,新的色彩查找表并不是由自然彩色图像生成,而是由一种特制的“环境具有自然色而目标具有高亮色”的红外/微光彩色融合图像生成。因此,这种新的色彩查找表与红外/微光双波段图像的光谱变化具有极好的相关性,可以实现准确映射。在合成这种特制的红外/微光彩色融合图像时,为了克服红外/微光双波段图像缺乏与普通可见光图像相近的光谱变化的缺陷,使用微光波段的纹理信息来获得自然色彩；为了克服自然色彩中缺少与热目标相对应的高亮色的缺陷,使用红外/微光的特征级融合图像作为亮度通道以保持目标区域与背景环境间的高对比度。最后通过实验证明这种彩色夜视图像具有环境呈现自然色、目标呈现高亮色的特点,可以使观察者在拥有良好环境感知度的同时对热目标也有极高的敏感度。
(4)基于全局推断保留映射的半监督降维方法研究。
提出一种充分利用未标记样本所隐含的鉴别信息的线性半监督降维方法GIP。该方法不同于以往基于局部几何特征的半监督降维方法,通过定义并使用一种未标记样本的全局结构：“全局鉴别结构”来求得投影矩阵,使得未标记样本所隐含的类别信息得到充分利用,以提升降维后数据的分类效果。为了推断未标记样本所隐含的类别信息,一种基于路径不相似度的测量被用来构造数据间的连接矩阵。通过在数据可视化、物体图像识别数据库、人脸识别数据库、声音识别数据库上的实验证明该方法的有效性。最后也通过实验说明GIPP在微光图像彩色夜视成像中的应用：基于自动相关反馈结构,实现用于色彩传递的彩色参考图像的自动选择。
(5)基于Double Lomax先验的稀疏线性模型的变分贝叶斯求解方法研究。
提出一种新的“稀疏提升”型先验分布：Double Lomax先验,首先证明它代表了比ARD先验更逼近于e0范数的松弛函数,因此理论上它可以利用更少的测量值个数来恢复稀疏变量；然后证明它像ARD先验一样可以被表示成为高斯混合尺度模型(GSM)的形式,因此计算上它可以得到闭合解,同时兼顾了理论优越性和计算可行性。另外,推导出使用该先验的线性稀疏模型(SLM)的全变分贝叶斯求解方法,并分析说明该全变分贝叶斯求解方法可以很好地克服在使用非凸松弛型先验时会出现的“多个局部极值点”及“过学习”的问题。通过在自回归模型(AR)系统识别及压缩感知(CS)方面的实验说明该方法与基于ARD先验的方法相比的优越性。最后也通过实验说明该方法在单波段红外热像彩色夜视成像中的应用：可以使用较少的样本就训练出准确的色彩估计模型。
With optical-electro imaging devices, the night vision technology expands the range of human spectral response and enhances the ability to observe in the dark, resulting in "transparent night". Until recently, the standard representation of night vision imagery is monochrome, which is disadvantageous to scene interpretation and target detection. However, with the deeper understanding of the color's role in human visual perception, there is a growing interest in displaying night vision imagery with colors, especially with the natural colors that are consistent with human visual properties. The natural color night vision technology allows night vision images to obtain the best observing performances, thus has demonstrated important applications in both military and civilian areas, such as battlefield surveillance, intelligence transmission, criminal investigation, safety inspection, traffic control, night navigation, historic preservation.
In this thesis, discussions on the natural color night vision technology are conducted in two aspects. The first aspect is focusing on the properties of night vision imaging and application, discussing about the problem of depth perception enhancement in night vision imagery, the problem of colorization for single band thermal images, and the problem of real-time colorization for visible/thermal images. The second aspect is focusing on the related mathematical models for the natural color night vision technology, discussing about the dimensionality reduction method for image recognition, and the sparse learning method for model training. Specifically, the main contributions and innovations of this thesis are as follows:
(1) Study on Coloring night vision imagery for depth perception.
Depth perception for night vision imagery is important for scene comprehension. A novel scheme is proposed to give multiband night vision imagery a natural color appearance with depth sense. The scheme simulates the depth cue by varying saturation value of each color object, in correspondence with its relative depth value that is estimated from the ratio between infrared and low-light-level sensor outputs. In the proposed scheme, a night vision pattern database is built in advance, and employed to recognize the night vision objects based on emitting-reflecting and texture features. Then, each object is provided its correspondence natural color, and the saturation value of the natural color is varied according to the estimated depth information. Experimental results show that the proposed scheme can achieve satisfying results, provides night vision imagery smoothly natural color appearance as well as the sense of depth, thereby the situational awareness and target detection can be improved.
(2) Study on Colorizing Single Band Thermal Night vision Images.
Consider the problem of assigning single-band thermal night vision image with natural day-time color appearance. Modeling color distribution of thermal imagery is a challenging problem, since there are insufficient local features for estimating the chromatic value at a point. The proposed color estimation model incorporates multi-scale and spatially arranged image features, both linear and nonlinear model (SVR) are discussed. A supervised learning procedure is first employed to estimate colors of monochromic images, since the irrelevant luminance between thermal image and natural image. Different from current color night vision methods that based on "multi-band night vision image fusion", the proposed approach can be directly applied on single-band thermal image, so that the portability of night vision system can be enhanced. Experimental results show that the proposed approach leads to relatively accurate description of the desired color distribution.
(3) Study on Real-time Color Night-vision for Visible and Thermal images.
A real-time scheme to display the visible and thermal images in a fused representation with natural daylight color background and highlight thermal targets is proposed. The scheme is based on a specially designed "natural- highlight color" lookup-table (LUT). The LUT is derived from the combination of a visible-thermal image and its daylight-background-highlight-targets fused representation. To form this representation, the grayscale visible image is first transferred daylight colors by using the natural color transfer technique based on local texture information, then the luminance component of this colorized visible image is replaced with the feature level fusion image that optimized the target region feature of thermal image and the texture feature of visible image by using K-means clustering algorithm and discrete wavelet transform (DWT). Once the LUT has been derived the color mapping can be applied to different images and deployed in real-time. Experimental results show that the proposed scheme can achieve satisfying results, the overall scene recognition and situational awareness can be improved.
(4) Study on Global Inference Preserving Projection for Semi-supervised Discriminant Analysis.
A new linear dimensionality reduction approach, Global Inference Preserving Projection (GIPP), is proposed to perform classification task in semi-supervised case. It is based on a new global structure, which reveals the underlying discriminative knowledge of unlabeled samples. A path-based dissimilarity measurement is used to infer the underlying class information for unlabeled samples. Experimental results on data visualization, object recognition database, face recognition database and spoken letter recognition database demonstrate the effectiveness of the proposed approach. Moreover, it is shown that the proposed approach can be successfully applied in colorizing low-light-level night vision image, in the sense that the natural color reference image for color transfer can be automatically and efficiently selected.
(5) Study on A Variational Bayesian Approach to Sparse Linear Model Based on Double Lomax Priors.
A new family of sparsity-promoting prior coined to as Double Lomax prior is proposed. It is shown that on one hand it provides a tighter approximation to the L0 norm than ARD prior thus has theoretical superior for recovering sparse vectors with fewer measurements; on the other hand it owns Gaussian Scale Mixture representation thus has computational tractability for efficient Bayesian processing. A full Variational Bayesian inference is developed here to solve for SLM using Double Lomax priors. Being a strictly log-convex prior, Double Lomax prior brings challenges in inference procedure, such as multi-mode and asymmetrical posteriors. Analysis shows that the Variational Bayesian inference developed here is needed for avoiding local minimum and over-fitting. Experiments on both correlated and uncorrelated SLM simulations with applications to AR model identification and compressive sensing have demonstrated the effectiveness of the proposed approach. Moreover, it is shown that the proposed approach can be successfully applied in colorizing single band thermal night vision images, in the sense that fewer training samples are needed.
本论文首先从夜视图像的成像特性和应用特性出发,探讨具有景物深度及空间立体视觉感的自然彩色夜视实现的问题、单波段红外热像的自然彩色夜视实现的问题、红外/微光双波段图像的实时自然彩色夜视实现的问题；其次从与自然彩色夜视实现过程相关的数学模型出发,探讨用于图像识别的数据降维算法和用于模型训练的稀疏学习算法。具体而言,论文的主要研究内容和创新点如下：
(1)具有景物深度及空间立体视觉感的自然彩色夜视实现方法研究。
提出夜视图像应具有立体空间感的新问题。并基于夜视图像的特性,提出利用单目双波段夜视图像中的红外/微光的强度来估计景物的深度信息,再利用色彩饱和度变化来增强夜视图像空间感的方法。该方法首先通过构建一个夜视图像的模式数据库,利用辐射/反射特征和纹理特征来识别图像中的景物类别、并为各景物赋予其相应的特征色彩,再根据估计的深度信息对景物色彩的饱和度进行调制,以便获得符合人眼观察规律的具有空间立体视觉感的彩色夜视图像视觉。最后通过实验证明这种彩色夜视图像不仅具有与人眼视觉特性相吻合的自然色彩,更具有与景物深度相吻合的空间立体视觉感,从而可以显著改善观察者对夜视图像的理解效果、提高目标识别率。
(2)单波段红外热像的自然彩色夜视实现方法研究。
提出基于单波段红外热像实现自然彩色夜视的新问题。为克服红外图像缺乏细节、局部信息不足的问题,提出使用一种具有“多尺度”和“空间上下文”信息的特征向量来对像素点进行分析；为解决红外图像的亮度分布规律与可见光图像极为不同的问题,首次采用“监督学习”的方法来建立色彩估计模型,给出一种基于线性回归的线性模型和一种基于支持向量回归(SVR)的非线性模型。该方法不同于以往的基于“多波段图像融合技术”的彩色夜视实现方法,可直接基于单波段红外热像实现彩色夜视,在提升彩色夜视系统便携性、降低成本等方面具有重大价值。最后通过实验证明该方法可以有效地实现单波段红外热像的自然彩色夜视。
(3)红外/微光双波段图像的实时自然彩色夜视实现方法研究。
为了克服以往的实时彩色夜视实现方法不适用于红外/微光双波段图像的问题,根据红外/微光双波段图像的特性设计了一种新的“自然—高亮色彩查找表”。不同于以前的方法,新的色彩查找表并不是由自然彩色图像生成,而是由一种特制的“环境具有自然色而目标具有高亮色”的红外/微光彩色融合图像生成。因此,这种新的色彩查找表与红外/微光双波段图像的光谱变化具有极好的相关性,可以实现准确映射。在合成这种特制的红外/微光彩色融合图像时,为了克服红外/微光双波段图像缺乏与普通可见光图像相近的光谱变化的缺陷,使用微光波段的纹理信息来获得自然色彩；为了克服自然色彩中缺少与热目标相对应的高亮色的缺陷,使用红外/微光的特征级融合图像作为亮度通道以保持目标区域与背景环境间的高对比度。最后通过实验证明这种彩色夜视图像具有环境呈现自然色、目标呈现高亮色的特点,可以使观察者在拥有良好环境感知度的同时对热目标也有极高的敏感度。
(4)基于全局推断保留映射的半监督降维方法研究。
提出一种充分利用未标记样本所隐含的鉴别信息的线性半监督降维方法GIP。该方法不同于以往基于局部几何特征的半监督降维方法,通过定义并使用一种未标记样本的全局结构：“全局鉴别结构”来求得投影矩阵,使得未标记样本所隐含的类别信息得到充分利用,以提升降维后数据的分类效果。为了推断未标记样本所隐含的类别信息,一种基于路径不相似度的测量被用来构造数据间的连接矩阵。通过在数据可视化、物体图像识别数据库、人脸识别数据库、声音识别数据库上的实验证明该方法的有效性。最后也通过实验说明GIPP在微光图像彩色夜视成像中的应用：基于自动相关反馈结构,实现用于色彩传递的彩色参考图像的自动选择。
(5)基于Double Lomax先验的稀疏线性模型的变分贝叶斯求解方法研究。
提出一种新的“稀疏提升”型先验分布：Double Lomax先验,首先证明它代表了比ARD先验更逼近于e0范数的松弛函数,因此理论上它可以利用更少的测量值个数来恢复稀疏变量；然后证明它像ARD先验一样可以被表示成为高斯混合尺度模型(GSM)的形式,因此计算上它可以得到闭合解,同时兼顾了理论优越性和计算可行性。另外,推导出使用该先验的线性稀疏模型(SLM)的全变分贝叶斯求解方法,并分析说明该全变分贝叶斯求解方法可以很好地克服在使用非凸松弛型先验时会出现的“多个局部极值点”及“过学习”的问题。通过在自回归模型(AR)系统识别及压缩感知(CS)方面的实验说明该方法与基于ARD先验的方法相比的优越性。最后也通过实验说明该方法在单波段红外热像彩色夜视成像中的应用：可以使用较少的样本就训练出准确的色彩估计模型。
With optical-electro imaging devices, the night vision technology expands the range of human spectral response and enhances the ability to observe in the dark, resulting in "transparent night". Until recently, the standard representation of night vision imagery is monochrome, which is disadvantageous to scene interpretation and target detection. However, with the deeper understanding of the color's role in human visual perception, there is a growing interest in displaying night vision imagery with colors, especially with the natural colors that are consistent with human visual properties. The natural color night vision technology allows night vision images to obtain the best observing performances, thus has demonstrated important applications in both military and civilian areas, such as battlefield surveillance, intelligence transmission, criminal investigation, safety inspection, traffic control, night navigation, historic preservation.
In this thesis, discussions on the natural color night vision technology are conducted in two aspects. The first aspect is focusing on the properties of night vision imaging and application, discussing about the problem of depth perception enhancement in night vision imagery, the problem of colorization for single band thermal images, and the problem of real-time colorization for visible/thermal images. The second aspect is focusing on the related mathematical models for the natural color night vision technology, discussing about the dimensionality reduction method for image recognition, and the sparse learning method for model training. Specifically, the main contributions and innovations of this thesis are as follows:
(1) Study on Coloring night vision imagery for depth perception.
Depth perception for night vision imagery is important for scene comprehension. A novel scheme is proposed to give multiband night vision imagery a natural color appearance with depth sense. The scheme simulates the depth cue by varying saturation value of each color object, in correspondence with its relative depth value that is estimated from the ratio between infrared and low-light-level sensor outputs. In the proposed scheme, a night vision pattern database is built in advance, and employed to recognize the night vision objects based on emitting-reflecting and texture features. Then, each object is provided its correspondence natural color, and the saturation value of the natural color is varied according to the estimated depth information. Experimental results show that the proposed scheme can achieve satisfying results, provides night vision imagery smoothly natural color appearance as well as the sense of depth, thereby the situational awareness and target detection can be improved.
(2) Study on Colorizing Single Band Thermal Night vision Images.
Consider the problem of assigning single-band thermal night vision image with natural day-time color appearance. Modeling color distribution of thermal imagery is a challenging problem, since there are insufficient local features for estimating the chromatic value at a point. The proposed color estimation model incorporates multi-scale and spatially arranged image features, both linear and nonlinear model (SVR) are discussed. A supervised learning procedure is first employed to estimate colors of monochromic images, since the irrelevant luminance between thermal image and natural image. Different from current color night vision methods that based on "multi-band night vision image fusion", the proposed approach can be directly applied on single-band thermal image, so that the portability of night vision system can be enhanced. Experimental results show that the proposed approach leads to relatively accurate description of the desired color distribution.
(3) Study on Real-time Color Night-vision for Visible and Thermal images.
A real-time scheme to display the visible and thermal images in a fused representation with natural daylight color background and highlight thermal targets is proposed. The scheme is based on a specially designed "natural- highlight color" lookup-table (LUT). The LUT is derived from the combination of a visible-thermal image and its daylight-background-highlight-targets fused representation. To form this representation, the grayscale visible image is first transferred daylight colors by using the natural color transfer technique based on local texture information, then the luminance component of this colorized visible image is replaced with the feature level fusion image that optimized the target region feature of thermal image and the texture feature of visible image by using K-means clustering algorithm and discrete wavelet transform (DWT). Once the LUT has been derived the color mapping can be applied to different images and deployed in real-time. Experimental results show that the proposed scheme can achieve satisfying results, the overall scene recognition and situational awareness can be improved.
(4) Study on Global Inference Preserving Projection for Semi-supervised Discriminant Analysis.
A new linear dimensionality reduction approach, Global Inference Preserving Projection (GIPP), is proposed to perform classification task in semi-supervised case. It is based on a new global structure, which reveals the underlying discriminative knowledge of unlabeled samples. A path-based dissimilarity measurement is used to infer the underlying class information for unlabeled samples. Experimental results on data visualization, object recognition database, face recognition database and spoken letter recognition database demonstrate the effectiveness of the proposed approach. Moreover, it is shown that the proposed approach can be successfully applied in colorizing low-light-level night vision image, in the sense that the natural color reference image for color transfer can be automatically and efficiently selected.
(5) Study on A Variational Bayesian Approach to Sparse Linear Model Based on Double Lomax Priors.
A new family of sparsity-promoting prior coined to as Double Lomax prior is proposed. It is shown that on one hand it provides a tighter approximation to the L0 norm than ARD prior thus has theoretical superior for recovering sparse vectors with fewer measurements; on the other hand it owns Gaussian Scale Mixture representation thus has computational tractability for efficient Bayesian processing. A full Variational Bayesian inference is developed here to solve for SLM using Double Lomax priors. Being a strictly log-convex prior, Double Lomax prior brings challenges in inference procedure, such as multi-mode and asymmetrical posteriors. Analysis shows that the Variational Bayesian inference developed here is needed for avoiding local minimum and over-fitting. Experiments on both correlated and uncorrelated SLM simulations with applications to AR model identification and compressive sensing have demonstrated the effectiveness of the proposed approach. Moreover, it is shown that the proposed approach can be successfully applied in colorizing single band thermal night vision images, in the sense that fewer training samples are needed.
引文
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