Image fusion scheme using a novel dual-channel PCNN in lifting stationary wavelet domain

This paper presents a new multi-source image fusion scheme based on lifting stationary wavelet transform (LSWT) and a novel dual-channel pulse-coupled neural network (PCNN). By using LSWT, we can calculate a flexible multiscale and shift-invariant representation of registered images. After decomposing the original images using LSWT, a new dual-channel pulse coupled neural network, which can overcome some shortcomings of original PCNN for image fusion and putout the fusion image directly, is proposed and used for the fusion of sub-band coefficients of LSWT. In this fusion scheme, a new sum-modified-laplacian(NSML) of the low frequency sub-band image, which represent the edge-feature of the low frequency sub-band image in SLWT domain, is presented and input to motivate the dual-channel PCNN. For the fusion of high frequency sub-band coefficients, a novel local neighborhood modified-laplacian (LNML) measurement is developed and used as external stimulus to motivate the dual-channel PCNN. This fusion scheme is verified on several sets of multi-source images, and the experiments show that the algorithms proposed in the paper can significantly improve image fusion performance, compared with the fusion algorithms such as traditional wavelet, LSWT, and LSWT-PCNN in terms of objective criteria and visual appearance.     

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.     

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.     

基于成像机理的小波包变换多聚焦图像融合

由于可见光成像系统的聚焦范围有限,因而在成像过程中,除聚焦良好的物体能生成清晰的图像外,该物体前后一定距离外的所有物体都将呈现不同程度的模糊.为了获得场景内所有物体均清晰的图像,在分析了多聚焦图像成像机理的基础上,提出了一种基于小波包变换的融合方法.它是将成像系统先聚焦在一部分对象上,得到其清晰的图像;然后再将其聚焦在另一部分对象上,得到另一清晰的图像;最后把这两幅实验图像加以融合,从而获得场景内所有物体均清晰的图像.实验结果表明,基于小波包变换的融合方法能够将信号的频带进行多层次划分,对高频成分也能进一步地分解,可有效综合多聚焦图像.     

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.     

一种基于方向窗特性的Contourlet域的多聚焦图像融合算法

针对同一场景多聚焦图像的融合问题,提出了一种基于方向区域特性的Contourlet域多聚焦图像融合算法.该算法对图像进行Contourlet变换,分解为不同尺度、不同方向的高低频子带|低频和高频子带分别采用方向区域的方差匹配度和能量作为融合规则|最后通过反变换得到融合图像.结果表明,所提出的方向区域方法能够更好地体现二维图像中的曲线或直线状边缘特征,是一种有效可行的图像融合算法.     

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.     

Image fusion based on nonsubsampled contourlet transform for infrared and visible light image

In this paper, a new method based on nonsubsampled contourlet transform (NSCT) is proposed to fuse the infrared image and the visible light image, which will produce a new fused image by which the target can be identified more easily. Firstly, two original images were decomposed into low frequency subband coefficients and the bandpass direction subband coefficients by using NSCT. Secondly, the selection of the low frequency subband coefficient and the bandpass direction subband coefficient is discussed in detail. The low frequency subband coefficients are selected based on the regional visual characteristics. For the selection of bandpass direction subband coefficients, this paper proposes a minimum regional cross-gradient method, and the cross-gradient is gained by calculating the gradient between the pixel of bandpass subbands and the adjacent pixel in the fused image of the low-frequency components. Comparison experiments have been performed on different image sets, and experimental results demonstrate that the proposed method performs better in both subjective and objective qualities.     

基于小波变换的不同融合规则的图像融合研究

提出了利用小波变换按照不同融合规则及融合算子去构造融合图像对应的小波系数,得到一种新的基于多尺度分解的像素的图像融合方法.通过对融合图像统计参数对比表明,所提出的基于小波变换的像素局部能量作为准则的融合效果更好,既可避免传统融合规则存在的信息损失,又提高了融合图像的空间分辨率和清晰度,融合图像符合人的视觉特性,更有利于机器视觉.     

Multifocus image fusion scheme using focused region detection and multiresolution

To solve the fusion problem of the multifocus images of the same scene, a novel algorithm based on focused region detection and multiresolution is proposed. In order to integrate the advantages of spatial domain-based fusion methods and transformed domain-based fusion methods, we use a technique of focused region detection and a new fusion method of multiscale transform (MST) to guide pixel combination. Firstly, the initial fused image is acquired with a novel multiresolution image fusion method. The pixels of the original images, which are similar to the corresponding initial fused image pixels, are considered to be located in the sharply focused regions. By this method, the initial focused regions can be determined, and the techniques of morphological opening and closing are employed for post-processing. Then the pixels within the focused regions in each source image are selected as the pixels of the fused image; meanwhile, the initial fused image pixels which are located at the focused border regions are retained as the pixels of the final fused image. The fused image is then obtained. The experimental results show that the proposed fusion approach is effective and performs better in fusing multi-focus images than some current methods.     

基于小波包变换的红外与可见光图像融合

针对红外与可见光图像特点,提出一种基于小波包变换的融合算法。该算法先对源图像进行小波包分解,得到低频分量和各带通方向子带分量,并对不同分量采用不同的融合规则进行融合处理,得到各融合系数,然后经小波包重构获得融合图像。该方法可提取源图像细节信息,取得较好的融合效果。     

基于区域分割和Counterlet变换的图像融合算法

提出了一种基于区域分割和Contourlet变换的图像融合算法。首先,对各源图像做区域分割,并利用区域能量比和区域清晰比的概念来度量和提取区域信息;然后,对各源图像进行多尺度非子采样Contourlet分解,分解后的高频部分采用绝对值取大算子进行融合,低频部分则采用基于区域的融合规则和算子进行融合;最后进行重构得到融合图像。对红外与可见光图像进行了融合实验,并与基于像素的àtrous小波变换和Contourlet变换的融合效果进行了比较。结果表明,采用本文算法的融合图像既保留了可见光图像的光谱信息,又继承了红外图像的目标信息,其熵值高于基于像素的融合方法约10%,交叉熵仅为基于像素的融合方法的1%左右。     

Multifocus image fusion and denoising scheme based on homogeneity similarity

A novel image fusion algorithm based on homogeneity similarity is proposed in this paper, aiming at solving the fusion problem of clean and noisy multifocus images. Firstly, the initial fused image is acquired with one multiresolution image fusion method. The pixels of the source images, which are similar to the corresponding initial fused image pixels, are considered to be located in the sharply focused regions. By this method, the initial focused regions are determined. In order to improve the fusion performance, morphological opening and closing are employed for post-processing. Secondly, the homogeneity similarity is introduced and used to fuse the clean and noisy multifocus images. Finally, the fused image is obtained by weighting the neighborhood pixels of the point of source images which are located at the focused region. Experimental results demonstrate that, for the clean multifocus image fusion, the proposed method performs better than some popular image fusion methods in both subjective and objective qualities. Furthermore, it can simultaneously resolve the image restoration and fusion problem when the source multifocus images are corrupted by the Gaussian white noise, and can also provide better performance than the conventional methods.     

一种基于区域特性的红外与可见光图像融合算法

提出了一种基于区域分割和à trous小波变换的红外与可见光图像融合算法.首先,对红外与可见光图像进行区域分割及区域关联,并按关联映射图所划分区域提取红外与可见光图像的的能量信息及梯度信息;然后,对红外与可见光图像进行多尺度à trous小波变换分解,分解后的低频部分按照文中所提出的区域能量比和区域清晰比指标进行区域融合,高频部分采用绝对值取大算子进行融合;最后进行重构得到融合图像.结果表明,该算法既可保持可见光图像的光谱信息,又可有效获取红外图像的热目标信息.     

基于二代curvelet变换的图像融合研究

曲波(Curvelet)作为一种新的多尺度分析方法比小波更加适合分析二维图像中的曲线或直线状边缘特征,而且具有更高的逼近精度和更好的稀疏表达能力.将curvelet变换引入图像融合,能够更好地提取原始图像的特征,为融合图像提供更多的信息.第二代curvelet理论的提出也使得其理论更易理解和实现.因此,提出了一种基于第二代curvelet变换的图像融合方法,首先将图像进行curvelet变换,然后在相应尺度上利用融合规则将curvelet系数融合,最后进行重构得到融合结果.对多聚焦图像进行了实验,采用均方误差、偏差指数和相关系数对融合结果进行了客观评价,并与基于小波变换的融合进行了比较,实验结果表明该方法除分解2层时与小波性能相当,取其他分解层数时均获得更好的融合效果.     

基于多方向多尺度变换的图像压缩算法

针对小波变换方向选择性差的局限,提出了一种多方向多尺度的的图像变换。圆对称滤波器组首先将图像分解为高频子带和低频子带,然后利用方向滤波器组将高频子带分解为多个方向子带,而对低频子带进行小波变换。多方向多尺度变换能以更稀疏的方式表示图像的边缘和纹理等几何特征,有利于图像压缩。在该变换基础上,结合迭代量化、嵌入式块截断编码(EBCOT)和集合分裂嵌入式块编码(SPECK)构建一种压缩算法。实验结果表明,对于纹理和边缘丰富的图像,压缩算法的性能相对于JPEG2000有明显地提高。     

Novel fusion method for visible light and infrared images based on NSST–SF–PCNN

The purpose of image fusion is to combine the source images of the same scene into a single composite image with more useful information and much better visual effects, which is undoubtedly suitable for further image processing tasks. This paper presented a novel fusion method for visible light and infrared images based on non-subsampled shearlet transform (NSST)–spatial frequency (SF)–pulse coupled neural network (PCNN). As a recently developed multi-resolution geometric analysis tool, NSST not only has remarked superiorities over other past conventional tools in terms of information capturing and computational costs saving, but also overcomes the lack of shift-invariance in shearlet transform (ST), so NSST applies to conducting the decompositions and reconstructions. Besides, traditional PCNN model is also upgraded to be an improved one called IPCNN in this paper to fuse the low-frequency and high-frequency subband coefficients. In the IPCNN structure, on the one hand, the value of the linking strength β is determined by the SF which represents the gradient features of the subband image; on the other hand, the time matrix is utilized to adaptively decide the iteration number of the IPCNN model, which is helpful to increase the function efficiency and save computational resources. Experimental results indicate that the proposed method performs well and has obvious superiorities over other current typical ones in both subjective visual performance and objective criteria.     

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.     

基于伪维格纳分布的图像融合

提出了一种基于伪维格纳分布(PWVD)的融合方法.利用一维N像素的滑动窗口在各个方向上对各待融合图像进行伪维格纳变换,选择均方根最大的方向为各待融合网像的PWVD分解方向,分解形成待融合图像不同频段的能量谱图,然后,针对各待融合不同频段的能量谱图,融合原则足高频段取区域能量最大,低频段取能量方差最大,形成具有不同频段的融合能量谱网,最后,对能量谱网进行PWVD逆变换,形成融合图像.对红外与可见光图像、多聚焦图像、电子计算机X射线断层扫描(CT)图像与磁共振(MR)图像和红外与合成孔径雷达(SAR)图像进行了融合实验,并对融合图像和待融合图像进行了信息熵对比.实验结果表明,采用本文算法的融合图像保留了待融合图像的绝大部分信息.     

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。