基于非采样Contourlet变换的遥感图像融合算法

为了使融合后的多光谱图像在尽可能保持原始多光谱图像光谱特性的同时,显著提高空间分辨力,提出了一种基于非采样Contourlet变换(NSCT)的遥感图像融合算法。算法首先对全色波段图像进行非采样Contourlet变换,得到全色波段图像的低频子带系数和各带通方向子带系数;然后针对多光谱图像的每一个波段,将其进行双线性插值后作为融合后多光谱图像的低频子带系数,对全色波段图像的各带通方向子带系数采用基于成像系统物理特性的注入模型(调整系数)进行局部调整后,作为融合后多光谱图像的各带通方向子带系数,从而得到融合后多光谱图像的非采样Contourlet变换系数;最后再经非采样Contourlet逆变换得到该波段具有高空间分辨力的多光谱图像。采用IKONOS卫星遥感图像进行了仿真实验,实验结果表明,该算法在光谱保留和空间质量提高方面优于其它传统的遥感图像融合算法。     

一种基于非下采样Contourlet变换多聚焦图像融合算法

针对现有小波类图像融合算法的不足,提出了一种基于非下采样Contourlet变换多聚焦图像融合算法,并在Contourlet域中引入了局部区域可见度以及局部方向能量的概念.针对低频子带系数和各带通方向子带系数分别提出了基于局部区域可见度以及基于局部方向能量的系数选择方案.通过对多聚焦图像融合的仿真实验,表明该算法相对于传统的基于离散小波变换和离散小波框架变换融合算法能够有效减少有用信息的丢失以及虚假信息的引入,同时能够从源图像中提取更多的有用信息并注入到融合图像中, 得到更好视觉效果和更优量化指标的融合图像.     

基于区域分维和非采样Contourlet变换的图像融合算法

针对源图像有用信息的提取,提出了基于区域分维和非下采样Contourlet变换相结合的红外与可见光图像融合算法.将图像的区域属性、区域大小、边缘强度以及纹理显著程度等特点用图像不同尺度上的区域分维进行描述,对于非下采样Contourlet变换低频系数,根据源图像不同尺度上的区域分维进行基于系数选择的融合.针对带通子带系数设计了系数局部匹配度算子,依据匹配度不同采用加权和系数选取相结合的融合规则.与其他常规融合方法进行比较,该算法可有效实现红外与可见光图像的融合.     

Multifocus image fusion scheme based on features of multiscale products and PCNN in lifting stationary wavelet domain

Multifocus image fusion aims at overcoming imaging cameras's finite depth of field by combining information from multiple images with the same scene. For the fusion problem of the multifocus image of the same scene, a novel algorithm is proposed based on multiscale products of the lifting stationary wavelet transform (LSWT) and the improved pulse coupled neural network (PCNN), where the linking strength of each neuron can be chosen adaptively. In order to select the coefficients of the fused image properly with the source multifocus images in a noisy environment, the selection principles of the low frequency subband coefficients and bandpass subband coefficients are discussed, respectively. For choosing the low frequency subband coefficients, a new sum modified-Laplacian (NSML) of the low frequency subband, which can effectively represent the salient features and sharp boundaries of the image in the LSWT domain, is an input to motivate the PCNN neurons; when choosing the high frequency subband coefficients, a novel local neighborhood sum of Laplacian of multiscale products is developed and taken as one type of feature of high frequency to motivate the PCNN neurons. The coefficients in the LSWT domain with large firing times are selected as coefficients of the fused image. Experimental results demonstrate that the proposed fusion approach outperforms the traditional discrete wavelet transform (DWT)-based, LSWT-based and LSWT-PCNN-based image fusion methods even though the source image is in a noisy environment in terms of both visual quality and objective evaluation.     

A multi-focus image fusion algorithm based on an improved dual-channel PCNN in NSCT domain

This paper presents a multi-focus image fusion algorithm based on dual-channel PCNN in NSCT domain. The fusion algorithm based on multi-scale transform is likely to produce the pseudo-Gibbs effects and it is not effective to fuse the dim or partial bright images. To solve these problems, this algorithm will get a number of different frequency sub-image of the two images by using the NSCT transform, the selection principles of different subband coefficients obtained by the NSCT decomposition are discussed in detail, and the images are fused based on the improved dual-channel PCNN in order to determine the band-pass sub-band coefficient, at last fused image is obtained by using the inverse NSCT transform. Fusion rules based on dual-channel PCNN are used to solve the complexity of the PCNN parameter settings and long computing time problems. The experimental results show that the algorithm has overcome the defects of the traditional multi-focus image fusion algorithm and improved the fusion effect.     

非下采样变换的红外与可见光图像融合

基于非下采样Contourlet变换（NSCT）,提出了一种红外和可见光图像融合算法。针对低频子带系数和各带通方向子带系数分别提出了基于图像物理特征的系数加权选择方式与基于区域能量匹配的系数选择方式,即低频基于区域梯度信息、高频基于区域特征因子的加权与选择结合的图像融合算法。实验结果表明：非下采样Contourlet变换具有较快的运算速度,且经非下采样变换后能量更加集中,可提供更多的图像信息。相对于基于像素的图像融合算法,本文的图像融合算法具有更高的融合性能,是一种更适合图像融合的多尺度几何分析（MGA）工具。     

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.     

Infrared and visible image fusion scheme based on NSCT and low-level visual features

Multi-scale transform (MST) is an efficient tool for image fusion. Recently, many fusion methods have been developed based on different MSTs, and they have shown potential application in many fields. In this paper, we propose an effective infrared and visible image fusion scheme in nonsubsampled contourlet transform (NSCT) domain, in which the NSCT is firstly employed to decompose each of the source images into a series of high frequency subbands and one low frequency subband. To improve the fusion performance we designed two new activity measures for fusion of the lowpass subbands and the highpass subbands. These measures are developed based on the fact that the human visual system (HVS) percept the image quality mainly according to its some low-level features. Then, the selection principles of different subbands are presented based on the corresponding activity measures. Finally, the merged subbands are constructed according to the selection principles, and the final fused image is produced by applying the inverse NSCT on these merged subbands. Experimental results demonstrate the effectiveness and superiority of the proposed method over the state-of-the-art fusion methods in terms of both visual effect and objective evaluation results.     

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。     

An adaptive fusion approach for infrared and visible images based on NSCT and compressed sensing

A novel nonsubsampled contourlet transform (NSCT) based image fusion approach, implementing an adaptive-Gaussian (AG) fuzzy membership method, compressed sensing (CS) technique, total variation (TV) based gradient descent reconstruction algorithm, is proposed for the fusion computation of infrared and visible images.Compared with wavelet, contourlet, or any other multi-resolution analysis method, NSCT has many evident advantages, such as multi-scale, multi-direction, and translation invariance. As is known, a fuzzy set is characterized by its membership function (MF), while the commonly known Gaussian fuzzy membership degree can be introduced to establish an adaptive control of the fusion processing. The compressed sensing technique can sparsely sample the image information in a certain sampling rate, and the sparse signal can be recovered by solving a convex problem employing gradient descent based iterative algorithm(s).In the proposed fusion process, the pre-enhanced infrared image and the visible image are decomposed into low-frequency subbands and high-frequency subbands, respectively, via the NSCT method as a first step. The low-frequency coefficients are fused using the adaptive regional average energy rule; the highest-frequency coefficients are fused using the maximum absolute selection rule; the other high-frequency coefficients are sparsely sampled, fused using the adaptive-Gaussian regional standard deviation rule, and then recovered by employing the total variation based gradient descent recovery algorithm.Experimental results and human visual perception illustrate the effectiveness and advantages of the proposed fusion approach. The efficiency and robustness are also analyzed and discussed through different evaluation methods, such as the standard deviation, Shannon entropy, root-mean-square error, mutual information and edge-based similarity index.     

基于可见光的多波段偏振图像融合新算法

采用了一种新的基于小波变换的偏振图像融合算法.首先,将两个波段中的每一波段三幅偏振图像利用小波变换分解成低频和高频部分,低频的小波系数平均值作为融合后的低频系数,高频细节系数根据不同区域特征选择方法以及对应输入图像小波系数的窗口区域方差来确定融合后高频小波系数,得到一个波段一幅图像.接着,将得到的图像再进行小波分解,采用低频图像的小波系数最小值作为融合后的低频系数,高频图像根据纹理一致性测度的纹理检测确定融合规则,用来调整高频小波系数,将来自不同图像的特征与细节融合在一起,并对融合图像质量进行了对比评价.实验结果表明,融合后的偏振图像不仅反映了场景的偏振信息,而且还包含了丰富的光谱信息,目标与背景的衬比度也得到了增强,为进一步的目标检测和识别提供了便利.     

A novel image fusion algorithm based on nonsubsampled shearlet transform

To overcome the shortcoming of traditional image fusion method based on multi-scale transform, a novel adaptive image fusion algorithm based on nonsubsampled shearlet transform (NSST) is proposed. Firstly, the NSST is utilized to decompose the source images on various scales and in different directions, and the low frequency sub-band and bandpass sub-band coefficients are obtained. Secondly, for the low frequency sub-band coefficients, the singular value decomposition method in the gradient domain is used to estimate the local structure information of image, and an adaptive ‘weighted averaging’ fusion rule based on the sigmoid function and the extracted features is presented. To improve the quality of fused image, a novel sum-modified-Laplacian (NSML), which can extract more useful information from source images, is employed as the measurement to select bandpass sub-band coefficients. Finally, the fused image is obtained by performing the inverse NSST on the combined coefficients. The proposed fusion method is verified on several sets of multi-source images, and the experimental results show that the proposed approach can significantly outperform the conventional image fusion methods in terms of both objective evaluation criteria and visual quality.     

基于平移不变剪切波变换域图像融合算法

针对传统基于多尺度变换的图像融合方法存在的缺点,提出了一种基于平移不变剪切波变换域的自适应图像融合新方法.首先,使用平移不变剪切波变换对源图像进行分解,得到低频子带及方向带通子带系数.然后,对于低频子带系数采用梯度域奇异值分解方法估计图像的局部结构信息,提出了基于提取的特征与S函数的可变加权融合策略;对于各方向带通子带系数,提出了一种基于改进的拉普拉斯能量和匹配的"加权平均"和选择相结合的系数选择策略.最后,对得到的融合系数进行逆变换得到融合图像.通过实验可以发现相比于传统的图像融合方法,本文方法得到了更高的客观指标,融合图像视觉效果更好.     

Technique for gray-scale visual light and infrared image fusion based on non-subsampled shearlet transform

A novel image fusion technique based on NSST (non-subsampled shearlet transform) is presented, aiming at resolving the fusion problem of spatially gray-scale visual light and infrared images. NSST, as a new member of MGA (multi-scale geometric analysis) tools, possesses not only flexible direction features and optimal shift-invariance, but much better fusion performance and lower computational costs compared with several current popular MGA tools such as NSCT (non-subsampled contourlet transform). We specifically propose new rules for the fusion of low and high frequency sub-band coefficients of source images in the second step of the NSST-based image fusion algorithm. First, the source images are decomposed into different scales and directions using NSST. Then, the model of region average energy (RAE) is proposed and adopted to fuse the low frequency sub-band coefficients of the gray-scale visual light and infrared images. Third, the model of local directional contrast (LDC) is given and utilized to fuse the corresponding high frequency sub-band coefficients. Finally, the final fused image is obtained by using inverse NSST to all fused sub-images. In order to verify the effectiveness of the proposed technique, several current popular ones are compared over three different publicly available image sets using four evaluation metrics, and the experimental results demonstrate that the proposed technique performs better in both subjective and objective qualities.     

Multifocus image fusion based on features contrast of multiscale products in nonsubsampled contourlet transform domain

In this paper, an efficient multifocus image fusion approach is proposed based on local features contrast of multiscale products in nonsubsampled contourlet transform (NSCT) domain. In order to improve the robustness of the fusion algorithm to the noise and select the coefficients of the fused image properly, the multiscale products, which can distinguish edge structures from noise more effectively in NSCT domain, is developed and introduced into image fusion field. The selection principles of different subband coefficients obtained by the NSCT decomposition are discussed in detail. To improve the quality of the fused image, novel different local features contrast measurements, which are proved to be more suitable for human vision system and can extract more useful detail information from source images and inject them into the fused image, are developed and used to select coefficients from the clear parts of subimages to compose coefficients of fused images. Experimental results demonstrate the proposed method performs very well in fusion both noisy and noise-free multifocus images, and outperform conventional methods in terms of both visual quality and objective evaluation criteria.     

基于区域检测与非下采样轮廓波变换的红外与彩色可见光图像融合

针对灰度图像融合的分辨率低及现有的彩色图像融合方法融合的图像色彩不自然、不符合人的视觉感受的特点,在此提出一种基于Snake模型的区域检测和非下采样轮廓波变换(NSCT)的红外与彩色可见光图像融合的方法。首先对彩色可见光图像进行亮度、色度和饱和度(IHS)颜色空间变换提取亮度分量,并用Snake模型对红外图像的目标区域进行检测;然后对亮度分量和目标替换的红外图像应用NSCT分解,对所得到的高频系数采用像素点"绝对值和取大"、低频系数采用基于"亮度重映射技术"的加权融合规则进行融合;通过对融合系数进行NSCT逆变换获得融合图像的亮度分量,最后运用颜色空间逆变换得到融合图像。实验结果表明,所提出的融合方法既能保持可见光图像的高分辨率和自然色彩,又能准确保留红外图像中检测出的目标信息,获得视觉效果较好、综合指标较优的融合图像。     

一种基于NSCT变换的红外与可见光图像融合算法

针对红外与可见光图像融合,提出了一种基于NSCT变换的图像融合方法。对经NSCT变换的低频子带系数采用基于区域能量自适应加权的融合规则,对高频子带系数采用混合的融合方法,即对于低层,采用基于区域方差选大的融合方法,对于高层采用像素点的绝对值选大的融合方法。实验结果表明,该融合算法可以获得更多的细节信息,能获得较理想的融合图像。     

基于嵌入式多尺度分解和可能性理论的多波段纹理图像融合

将多尺度变换和“高频取大、低频加权平均”融合规则相结合是融合双波段图像的有效方法。但用该类方法融合多波段图像时,序贯式加权常常会导致原图像间固有的差异信息在融合图像中被弱化,从而影响后续的目标识别和场景理解。该问题在融合具有纹理特征的多波段图像时更为突出。为此,提出了一个基于嵌入式多尺度分解和可能性理论的多波段纹理图像融合新方法。首先,利用一种多尺度变换方法把多波段原图像分别分解为高频和低频成分,并对多波段图像中标准差最大的一幅原图像的低频成分利用另一种多尺度方法进行分块,再以该分块图像的大小和位置为标准对其余波段的原图像进行分块。然后,基于可能性理论的相关融合规则逐一融合对应的多波段块图像,再把块融合图像进行拼接,以拼接结果作为低频融合图像。最后,将该低频融合图像和利用取大规则融合得到的高频成分一起通过多尺度逆变换获得最终的融合图像。这种方法不仅将像素级和特征级融合方法综合在一起, 而且将空间域和变换域技术综合在一起, 并通过对大小块采用不同融合规则解决了目标边缘的锯齿效应问题。实验表明该方法效果显著。     

基于数学形态学的Contourlet变换域图像降噪方法

提出了一种基于数学形态学的Contourlet变换域图像降噪方法.首先对输入的带噪图像进行多尺度、多方向的Contourlet稀疏变换,然后利用数学形态学算子在Contourlet域对高频系数进行处理,去除图像中具有较小支撑域的噪音,有效保留具有连续支撑域的图像边缘信息.最后通过Contourlet反变换得到预降噪图像.仿真结果表明,该方法较一般的Contourlet域收缩阈值降噪方法的降噪效果好,提高了PSNR值并降低了MSE值,获得更好的图像恢复质量.     

Image fusion based on expectation maximization algorithm and steerable pyramid

In this paper, a novel image fusion method based on the expectation maximization (EM) algorithm and steerable pyramid is proposed. The registered images are first decomposed by using steerable pyramid.The EM algorithm is used to fuse the image components in the low frequency band. The selection method involving the informative importance measure is applied to those in the high frequency band. The final fused image is then computed by taking the inverse transform on the composite coefficient representations.Experimental results show that the proposed method outperforms conventional image fusion methods.