Fusion of infrared-visible images using improved multi-scale top-hat transform and suitable fusion rules

Integration of infrared and visible images is an active and important topic in image understanding and interpretation. In this paper, a new fusion method is proposed based on the improved multi-scale center-surround top-hat transform, which can effectively extract the feature information and detail information of source images. Firstly, the multi-scale bright (dark) feature regions of infrared and visible images are respectively extracted at different scale levels by the improved multi-scale center-surround top-hat transform. Secondly, the feature regions at the same scale in both images are combined by multi-judgment contrast fusion rule, and the final feature images are obtained by simply adding all scales of feature images together. Then, a base image is calculated by performing Gaussian fuzzy logic combination rule on two smoothed source images. Finally, the fusion image is obtained by importing the extracted bright and dark feature images into the base image with a suitable strategy. Both objective assessment and subjective vision of the experimental results indicate that the proposed method is superior to current popular MST-based methods and morphology-based methods in the field of infrared-visible images fusion.     

A categorization method of infrared polarization and intensity image fusion algorithm based on the transfer ability of difference features

The fusion of infrared polarization and intensity image can significantly improve the detection performance of target, and the fused image is more suitable for human visual perception and further image-processing tasks. In this paper, a new categorization method of infrared polarization and intensity image fusion algorithm based on the transfer ability of difference feature is proposed. Firstly, the difference feature between two kinds of image and the characteristics of different fusion algorithms are analyzed and summarized. Second, an evaluation vector of fusion algorithm for difference feature transform ability is constructed. Thirdly, the transfer ability of fusion algorithm for difference feature is estimated by the evaluation vector, and the degree of transfer ability of fusion algorithm for difference feature is analyzed. Finally the fusion algorithms are classified by the degree of transfer ability of fusion algorithm for difference feature. The results shows that the proposed fusion algorithm categorization method helps select fusion algorithms in actual scene.     

Fusion method for infrared and visible images by using non-negative sparse representation

In this paper, an interesting fusion method, named as NNSP, is developed for infrared and visible image fusion, where non-negative sparse representation is used to extract the features of source images. The characteristics of non-negative sparse representation coefficients are described according to their activity levels and sparseness levels. Multiple methods are developed to detect the salient features of the source images, which include the target and contour features in the infrared images and the texture features in the visible images. The regional consistency rule is proposed to obtain the fusion guide vector for determining the fused image automatically, where the features of the source images are seamlessly integrated into the fused image. Compared with the classical and state-of-the-art methods, our experimental results have indicated that our NNSP method has better fusion performance in both noiseless and noisy situations.     

Fusion method of infrared and visible images based on neighborhood characteristic and regionalization in NSCT domain

On fusing infrared and visible image, the traditional fusion method cannot get the better image quality. Based on neighborhood characteristic and regionalization in NSCT (Nonsubsampled Contourlet Transform) domain, the fusion algorithm was proposed. Firstly, NSCT was adopted to decompose infrared and visible images at different scales and directions for the low and high frequency coefficients, the low frequency coefficients which were fused with improving regional weighted fusion method based on neighborhood energy, and the high-frequency coefficients were fused with multi-judgment rule based on neighborhood characteristic regional process. Finally, the coefficients were reconstructed to obtain the fused image. The experimental results show that, compared with the other three related methods, the proposed method can get the biggest value of IE (information entropy), MI(VI,F) (mutual information from visible image), MI(VI,F) (mutual information from infrared image), MI (sum of mutual information), and Q^AB/F (edge retention). The proposed method can leave enough information in the original images and its details, and the fused images have better visual effects.     

Fusion of visible and infrared images using global entropy and gradient constrained regularization

Infrared and visible image fusion has been an important and popular topic in imaging science. Dual-band image fusion aims to extract both target regions in infrared image and abundant detail information in visible image into fused result, preserving even enhancing the information that inherits from source images. In our study, we propose an optimization-based fusion method by combining global entropy and gradient constrained regularization. We design a cost function by taking the advantages of global maximum entropy as the first term, together with gradient constraint as the regularized term. In this cost function, global maximum entropy could make the fused result inherit as more information as possible from sources. And using gradient constraint, the fused result would have clear details and edges with noise suppression. The fusion is achieved based on the minimization of the cost function by adding weight value matrix. Experimental results indicate that the proposed method performs well and has obvious superiorities over other typical algorithms in both subjective visual performance and objective criteria.     

一种红外偏振图像的二次融合方法

根据红外偏振特性图像的冗余性与互补性,提出了一种基于梯度特征和支持度变换的二次融合方法。利用提出的方法先对红外偏振度与偏振角图像进行梯度特征融合,然后利用支持度变换对梯度特征融合图像和合成强度图像进行二次融合,得到了边缘突出、轮廓清晰、对比度高的的融合图像。实验结果表明:融合图像与偏振角图像、偏振度图像、合成强度图像相比局部方差分别提高23.02%、176.9%、148.2%;对比度分别提高67.84%、196.5%、49.39%;平均梯度分别提高46.09%、164.1%、214.5%。     

一种基于特征分析的偏振图像融合方法

偏振成像探测能反映出传统光学成像所无法反映的物体的信息,为了克服计算偏振参量图像丢失细节信息的不足,在已有偏振图像融合方法的基础上,提出一种基于综合图像特征的融合方法。相较于已有算法突出图像的某一方面特征,该算法提取图像的灰度特征、纹理特征和形状特征,据此确定融合权值,对图像进行融合,能够较好地反映目标的细节信息,融合后的图像相较于普通光强图像,方差、信息熵分别提高了12.6%、17.5%,平均梯度从0.59提高到1.83。针对该方法用到的特征维数较高的问题,提供了一种简化算法,耗时从3054降至1337。     

An improved fusion algorithm for infrared and visible images based on multi-scale transform

In this paper, an improved fusion algorithm for infrared and visible images based on multi-scale transform is proposed. First of all, Morphology-Hat transform is used for an infrared image and a visible image separately. Then two images were decomposed into high-frequency and low-frequency images by contourlet transform (CT). The fusion strategy of high-frequency images is based on mean gradient and the fusion strategy of low-frequency images is based on Principal Component Analysis (PCA). Finally, the final fused image is obtained by using the inverse contourlet transform (ICT). The experiments and results demonstrate that the proposed method can significantly improve image fusion performance, accomplish notable target information and high contrast and preserve rich details information at the same time.     

基于小波框架的红外/可见光图像融合

针对小波框架所具有的冗余性和平移不变性在图像处理中能较好保留图像细节特征的特点,提出了一种将l2上的离散小波框架和交叉子带像素融合方案(Cross band pixel selection)结合起来的融合算法,并用于红外和可见光图像的融合。仿真试验表明该算法较好地保留了源图像的细节信息,是一种行之有效的融合算法。     

Correction of aeroheating-induced intensity nonuniformity in infrared images

Aeroheating-induced intensity nonuniformity effects severely influence the effective performance of an infrared (IR) imaging system in high-speed flight. In this paper, we propose a new approach to the correction of intensity nonuniformity in IR images. The basic assumption is that the low-frequency intensity bias is additive and smoothly varying so that it can be modeled as a bivariate polynomial and estimated by using an isotropic total variation (TV) model. A half quadratic penalty method is applied to the isotropic form of TV discretization. And an alternating minimization algorithm is adopted for solving the optimization model. The experimental results of simulated and real aerothermal images show that the proposed correction method can effectively improve IR image quality.     

基于DTCWT和稀疏表示的红外偏振与光强图像融合

针对红外偏振与光强图像彼此包含共同信息和特有信息的特点,提出了一种基于双树复小波变换和稀疏表示的图像融合方法.首先,利用双树复小波变换获取源图像的高频和低频成分,并用绝对值最大值法获得融合的高频成分;然后,用低频成分组成联合矩阵,并使用K-奇异值分解法训练该矩阵的冗余字典,根据该字典求出各个低频成分的稀疏系数,通过稀疏系数中非零值的位置信息判断共有信息和特有信息,并分别使用相应的规则进行融合;最后,将融合的高低频系数经过双树复小波反变换得到融合图像.实验结果表明,本文提出的融合算法不仅能较好地凸显源图像的共有信息,而且能很好地保留它们的特有信息,同时,融合图像具有较高的对比度和细节信息.     

Two-scale image fusion of visible and infrared images using saliency detection

Military, navigation and concealed weapon detection need different imaging modalities such as visible and infrared to monitor a targeted scene. These modalities provide complementary information. For better situation awareness, complementary information of these images has to be integrated into a single image. Image fusion is the process of integrating complementary source information into a composite image. In this paper, we propose a new image fusion method based on saliency detection and two-scale image decomposition. This method is beneficial because the visual saliency extraction process introduced in this paper can highlight the saliency information of source images very well. A new weight map construction process based on visual saliency is proposed. This process is able to integrate the visually significant information of source images into the fused image. In contrast to most of the multi-scale image fusion techniques, proposed technique uses only two-scale image decomposition. So it is fast and efficient. Our method is tested on several image pairs and is evaluated qualitatively by visual inspection and quantitatively using objective fusion metrics. Outcomes of the proposed method are compared with the state-of-art multi-scale fusion techniques. Results reveal that the proposed method performance is comparable or superior to the existing methods.     

Mathematical principle and characteristics of polarization transform

The precise definition of polarization transform is proposed from the viewpoint of polarization domain transform for the first time. The mathematical principle, characteristics and physical meanings of polarization transform are analyzed. Two typical kinds of polarization transforms which are called unitary matrix polarization transform (UMPT) and diagonal matrix polarization transform (DMPT) are put forward. The gain property and the change property of polarization state distance are presented in detail. Thereby, the concept of serial polarization transform is proposed. A general polarization transform can be realized by two steps: changing the location of polarization states in polarization domain by UMPT; and expanding or compressing the density of polarization states by DMPT. The result is meaningful to many applications including polarization filtering, detection, enhancement, anti-jamming, discrimination and so on. Supported in part by the National Excellent PhD Dissertations Fund of China (Grant No. 08100101), the New Century Excellent Talents Fund of China (Grant No. NCET-04-0997), the National Natural Science Foundation of China (Grant No. 60672033) and the Excellent PhD Fund of National University of Defense Technology (Grant No. B070401)     

一种基于边缘的曲波变换图像融合方法

提出了一种新的图像融合方法。采用Canny边缘检测方法,并结合曲波变换算法,确定图像边缘位置。针对曲波分解的不同频率域,对高频信息依据其是否为边缘点采用不同的融合策略,对低频信息利用平均法进行融合,最后进行重构得到融合结果。对该方法得到的实验结果进行主客观评价和对比表明,提出的算法在抑制噪声的同时,能有效地突出边缘细节,具有更好的视觉效果。     

Fusion for visible and infrared images using visual weight analysis and bilateral filter-based multi scale decomposition

Fusion for visible and infrared images has been an important and challenging work in image analysis. Both the feature information in infrared image and abundant detail information in visible image should be preserved and enhanced in fused result. In this paper, a detail enhanced fusion algorithm through visual weight analysis based on smooth-inspired multi scale decomposition is proposed. With variable parameter, bilateral filter-based idea successfully decomposes the two source image into several scales. At each scale level, visual weight map is calculated and used for fusion. Finally, those levels are synthetized with proper weights. Using this idea, the detail information could be enhanced easily. The experimental results demonstrate the proposed approach performs better than other methods, especially in visual effect and keeping details.     

Region-based fusion of infrared and visible images using nonsubsampled contourlet transform

With the nonsubsampled contourlet transform (NSCT), a novel region-segmentation-based fusion algorithm for infrared (IR) and visible images is presented.The IR image is segmented according to the physical features of the target.The source images are decomposed by the NSCT, and then, different fusion rules for the target regions and the background regions are employed to merge the NSCT coefficients respectively.Finally, the fused image is obtained by applying the inverse NSCT.Experimental results show that the proposed algorithm outperforms the pixel-based methods, including the traditional wavelet-based method and NSCT-based method.     

基于Tetrolet变换的红外与可见光融合

针对目前红外与可见光图像融合速度慢、 融合结果对比度不高且易产生伪影的缺点,提出一种基于Tetrolet变换的改进融合算法。首先,将可见光图像转换到lαβ颜色空间得到三个几乎不相关的彩色通道;然后对其l分量和红外图像分别进行Tetrolet变换,对于低通系数引入邻域能量及其接近度的融合规则。而对Tetrolet系数采用伪随机傅里叶矩阵进行观测并加权融合其观测值;接下来对融合后观测值采用CoSaMP优化算法迭代出融合后的Tetrolet系数,并经Tetrolet重构得到融合后的灰度图像;最后将灰度图像映射到RGB颜色空间获得最终的融合图像。实验证明了本文算法的有效性。     

基于支持度变换和top-hat分解的双色中波红外图像融合

为了解决用多尺度top-hat分解法融合双色中波红外图像时经常存在对比度提升有限、边缘区域失真较重的问题,提出了基于支持度变换和top-hat分解相结合的融合方法。先用支持度变换法将双色中波图像分解为低频图像和支持度图像序列;再从最后一层低频图像中用多尺度top-hat分解法提取各自的亮信息和暗信息;用灰度值取大法分别融合亮信息和暗信息;通过灰度值归一化和高斯滤波分别增强亮、暗信息融合图像;然后融合两低频图像和亮、暗信息增强图像;将融合图像作为新的低频图像和用灰度值取大法融合得到的支持度融合图像序列进行支持度逆变换,得到最终融合图像。该方法的实验结果同采用单一的支持度变换法融合和多尺度top-hat分解法融合相比,融合图像的对比度提升了11.69%,失真度降低了63.42%,局部粗糙度提高了38.12%。说明提出的从低频图像提取亮暗信息,并经过分别融合、增强,再与低频图像进行融合,能有效破解红外融合图像对比度提升和边缘区域失真度降低之间的矛盾,为提高图像融合质量提供了新方法。     

Sharpening enhancement of ultrasound images using contourlet transform

The furry regions of ultrasound images are to be enhanced for good quality visual perception. This paper proposes a contourlet transform (CT) based sharpening technique (ST) for contrast enhancement in ultrasound (US) images. While sharpening, noise emphasize is the drawback of the classical ST methods. The proposed ST is operated on the multiscale, multidirectional CT decomposition of the underlying US image. The new ST not only sharpens the US image but also control the noise effect with tunable parameters. The results are compared with common unsharp masking and recently proposed nonlinear unsharp masking. The parameters like enhancement measure, structural similarity, and blind image quality measure evaluate the improved performances of the proposed technique.