对称分数B样条小波的PCA图像融合

有良好逼近能力的对称分数B样条小波,在刻画图像纹理方面优于传统小波,为图像融合提供了有利条件。将其与PCA（Principal Component Analysis）变换相结合之后对高分辨率全色图像和低分辨率多光谱图像进行融合,提出了一种新的图像融合算法。对两幅源图像应用PCA变换,得到的两个第一主分量分别进行对称分数B样条小波变换,再对产生的两组高、低频小波系数采取不同的规则进行融合,生成两组新的高、低频系数,对其进行小波反变换得到新的第一主分量,与多光谱图像的其他主分量进行PCA反变换,得到最终的融合图像。实验结果表明,该方法使融合图像既提高了分辨率又保留了丰富的光谱信息。     

Remote sensing image fusion based on Bayesian linear estimation

A new remote sensing image fusion method based on statistical parameter estimation is proposed in this paper. More specially, Bayesian linear estimation (BLE) is applied to observation models between remote sensing images with different spa- tial and spectral resolutions. The proposed method only estimates the mean vector and covariance matrix of the high-resolution multispectral (MS) images, instead of assuming the joint distribution between the panchromatic (PAN) image and low-resolution multispectral image. Furthermore, the proposed method can enhance the spatial resolution of several principal components of MS images, while the traditional Principal Component Analysis (PCA) method is limited to enhance only the first principal component. Experimental results with real MS images and PAN image of Landsat ETM demonstrate that the proposed method performs better than traditional methods based on statistical parameter estimation, PCA-based method and wavelet-based method.     

基于LP融合和亮度变换的图像增强

将图像融合与亮度变换结合,提出一种图像增强的新方法。该方法利用拉普拉斯塔融合,在亮度变换中引入克隆选择算法优化参数,取得较好的增强效果。与PCA方法和小波方法相比,图像的信息熵保持在较高水平,平均梯度值分别提高3．28和0．14左右,标准差平均提高6．92和5．14左右。     

基于FPDE的红外与可见光图像融合算法

针对传统红外与可见光图像融合算法中存在的细节信息不够丰富, 边缘信息保留不够充分等问题, 文中提出了一种基于四阶偏微分方程(Fourth-order partial differential equation, FPDE)的改进的图像融合算法.算法首先采用FPDE将已配准的红外与可见光图像进行分解, 得到高频分量和低频分量; 然后, 对高频分量采用基于主成分分析(Principal component analysis, PCA)的融合规则来得到细节图像, 对低频分量采用基于期望值最大(Expectation maximization, EM)的融合规则来得到近似图像; 最后, 通过组合最终的高频分量和低频分量来重构得到最终的融合结果.实验是建立在标准的融合数据集上进行的, 并与传统的和最近的融合方法进行比较, 结果证明所提方法得到的融合图像比现有的融合方法能有效地综合红外与可见光图像中的重要信息, 有更好的视觉效果.     

Mammogram Enhancement Using Lifting Dyadic Wavelet Transform and Normalized Tsallis Entropy

In this paper, we present a new technique for mammogram enhancement using fast dyadic wavelet transform （FDyWT） based on lifted spline dyadic wavelets and normalized Tsallis entropy. First, a mammogram image is decom- posed into a multiscale hierarchy of low-subband and high-subband images using FDyWT. Then noise is suppressed using normalized Tsallis entropy of the local variance of the modulus of oriented high-subband images. After that, the wavelet coefficients of high-subbands are modified using a non-linear operator and finally the low-subband image at the first scale is modified with power law transformation to suppress background. Though FDyWT is shift-invariant and has better poten- tial for detecting singularities like edges, its performance depends on the choice of dyadic wavclcts. On the other hand, the nulnber of vanishing moments is an important characteristic of dyadic wavelets for singularity analysis because it provides an upper bound measurement for singularity characterization. Using lifting dyadic schemes, we construct lifted spline dyadic wavelets of different degrees with increased number of vanishing moments. We also examine the effect of these wavelets on mammogram enhancement. The method is tested on mammogram images, taken from MIAS （Mammographic Image Analysis Society） database, having various background tissue types and containing different abnormalities. The comparison with tile state-of-the-art contrast enhancement methods reveals that the proposed method performs better and the difference is statistically significant.     

Remote-sensing image fusion using sparse representation with sub-dictionaries

Remote-sensing image fusion aims to obtain a multispectral (MS) image with a high spatial resolution, which integrates spatial information from the panchromatic (Pan) image and with spectral information from the MS image. Sparse representation (SR) has been recently used in remote-sensing image fusion method, and can obtain superior results to many traditional methods. However, the main obstacle is that the dictionary is generated from high resolution MS images (HRMS), which are difficult to acquire. In this article, a new SR-based remote-sensing image fusion method with sub-dictionaries is proposed. The image fusion problem is transformed into a restoration problem under the observation model with the sparsity constraint, so the fused HRMS image can then be reconstructed by a trained dictionary. The proposed dictionary for image fusion is composed of several sub-dictionaries, each of which is constructed from a source Pan image and its corresponding MS images. Therefore, the dictionary can be constructed without other HRMS images. The fusion results from QuickBird and IKONOS remote-sensing images demonstrate that the proposed method gives higher spatial resolution and less spectral distortion compared with other widely used and the state-of-the-art remote-sensing image fusion methods.     

基于Directionlets和PCA的多光谱与全色图像融合

基于数字化线段理论和整数栅格理论的Directionlets不仅继承了小波变换维数可分性的特点,而且通过选择变换方向和队列方向来获得灵活的多方向性,从而得到能够更好地捕获图像方向信息的方向各向异性的基函数。首先基于Directionlets和PCA的全色和多光谱图像融合方法,对多光谱图像进行线性PCA变换,并提取出其主分量;然后使用Directionlets提取高空间分辨率的全色图像的空间细节信息,将其"注入"到多光谱图像的主分量中。因此,得到的融合图像具有更多的多光谱图像的光谱信息和全色图像的空间信息。实验结果表明,在UIQI指数、整体图像质量指数Q4、平均梯度等主观视觉效果和客观评价指标上,新方法均优于基于小波变换的方法。     

A comparison study on fusion methods using evaluation indicators

Various fusion methods have been developed for improving data spatial resolution. The methods most encountered in the literature are the intensity‐hue‐saturation (IHS) transform, the Brovey transform, the principal components algorithm (PCA) fusion method, the Gram–Schmidt fusion method, the local mean matching method, the local mean and variance matching method, the least square fusion method, the discrete wavelet fusion method including Daubechies, Symlet, Coiflet, biorthogonal spline, reverse biorthogonal spline, and Meyer wavelets, the wavelet‐PCA fusion method, and the crossbred IHS and wavelet fusion method. Using various evaluation indicators such as two‐dimensional correlation, relative difference of means, relative variation, deviation index, entropy difference, peak signal‐to‐noise ratio index and universal image quality index, as well as photo‐interpretation methods and techniques, results of the above fusion methods were compared and comments on the fusion methods and potential of evaluation indicators were made. Among data fusion methods and indicators the local mean and variance matching methods proved the most efficient and the peak signal‐to‐noise ratio indicator proved the most appropriate for the evaluation of data fusion results.     

Comparison and improvement of wavelet‐based image fusion

The wavelets used in image fusion can be categorized into three general classes: orthogonal, biorthogonal, and non‐orthogonal. Although these wavelets share some common properties, each wavelet also has a unique image decomposition and reconstruction characteristic that leads to different fusion results. This paper focuses on the comparison of the image‐fusion methods that utilize the wavelet of the above three general classes, and theoretically analyses the factors that lead to different fusion results. Normally, when a wavelet transformation alone is used for image fusion, the fusion result is not good. However, if a wavelet transform and a traditional fusion method, such as an IHS transform or a PCA transform, are integrated, better fusion results may be achieved. Therefore, this paper also discusses methods to improve wavelet‐based fusion by integrating an IHS or a PCA transform. As the substitution in the IHS transform or the PCA transform is limited to only one component, the integration of the wavelet transform with the IHS or PCA to improve or modify the component, and the use of IHS or PCA transform to fuse the image, can make the fusion process simpler and faster. This integration can also better preserve colour information. IKONOS and QuickBird image data are used to evaluate the seven kinds of wavelet fusion methods (orthogonal wavelet fusion with decimation, orthogonal wavelet fusion without decimation, biorthogonal wavelet fusion with decimation, biorthogonal wavelet fusion without decimation, wavelet fusion based on the ‘à trous’, wavelet and IHS transformation integration, and wavelet and PCA transformation integration). The fusion results are compared graphically, visually, and statistically, and show that wavelet‐integrated methods can improve the fusion result, reduce the ringing or aliasing effects to some extent, and make the whole image smoother. Comparisons of the final results also show that the final result is affected by the type of wavelets (orthogonal, biorthogonal, and non‐orthogonal), decimation or undecimation, and wavelet‐decomposition levels.     

基于PCA和NSCT的多光谱图像和全色图像的融合

研究了主分量分析（PCA）和非下采样Contourlet变换（NSCT）,提出一种新的多光谱图像和全色图像的融合算法。该方法对多光谱图像进行PCA变换,对所得的第一主分量（PC1）以及全色图像进行NSCT变换。对二者的低频近似系数再次进行PCA变换以寻求多光谱信息和空间信息的平衡;对于高频细节系数,通过结构相似性指标（SSIM）和局部Sobel梯度进行融合,进一步提高空间信息量;经过逆NSCT和逆PCA变换得到融合图像。实验结果表明,提出的方法在增强融合图像空间细节表现能力的同时,尽可能地保留了多光谱图像的光谱信息,优于传统的基于IHS、PCA、小波变换和Contourlet变换的融合方法,是有效可行的。     

形态学滤波和改进PCNN的NSST域多光谱与全色图像融合

目的 全色图像的空间细节信息增强和多光谱图像的光谱信息保持通常是相互矛盾的,如何能够在这对矛盾中实现最佳融合效果一直以来都是遥感图像融合领域的研究热点与难点。为了有效结合光谱信息与空间细节信息,进一步改善多光谱与全色图像的融合质量,提出一种形态学滤波和改进脉冲耦合神经网络（PCNN）的非下采样剪切波变换（NSST）域多光谱与全色图像融合方法。方法 该方法首先分别对多光谱和全色图像进行非下采样剪切波变换;对二者的低频分量采用形态学滤波和高通调制框架（HPM）进行融合,将全色图像低频子带的细节信息注入到多光谱图像低频子带中得到融合后的低频子带;对二者的高频分量则采用改进脉冲耦合神经网络的方法进行融合,进一步增强融合图像中的空间细节信息;最后通过NSST逆变换得到融合图像。结果 仿真实验表明,本文方法得到的融合图像细节信息清晰且光谱保真度高,视觉效果上优势明显,且各项评价指标与其他方法相比整体上较优。相比于5种方法中3组融合结果各指标平均值中的最优值,清晰度和空间频率分别比NSCT-PCNN方法提高0.5%和1.0%,光谱扭曲度比NSST-PCNN方法降低4.2%,相关系数比NSST-PCNN方法提高1.4%,信息熵仅比NSST-PCNN方法低0.08%。相关系数和光谱扭曲度两项指标的评价结果表明本文方法相比于其他5种方法能够更好地保持光谱信息,清晰度和空间频率两项指标的评价结果则展示了本文方法具有优于其他对比方法的空间细节注入能力,信息熵指标虽不是最优值,但与最优值非常接近。结论 分析视觉效果及各项客观评价指标可以看出,本文方法在提高融合图像空间分辨率的同时,很好地保持了光谱信息。综合来看,本文方法在主观与客观方面均具有优于亮度色调饱和度（IHS）法、主成分分析（PCA）法、基于非负矩阵分解（CNMF）、基于非下采样轮廓波变换和脉冲耦合神经网络（NSCT-PCNN）以及基于非下采样剪切波变换和脉冲耦合神经网络（NSST-PCNN）5种经典及现有流行方法的融合效果。     

Curvelet变换的多波段遥感图像融合研究

提出一种基于Curvelet变换的多波段遥感图像融合算法。Curvelet变换具有比小波变换更好的边缘表达,因而更适合图像的融合处理。采用具有多尺度、多方向特点的Curvelet变换对多波段遥感图像像进行分解。对于低频系数采用平均融合算法,根据高频子图边缘分布差异,对于方向高频系数采用区域边缘检测和区域谱熵算法实现多波段遥感图像的融合处理。实验结果表明,提出的算法与传统算法相比在保留原始图像边缘和纹理信息同时,可以有效地取得较好的融合视觉效果。     

基于非子采样Contourlet变换的遥感图像融合算法

针对人类视觉特性, 以及全色高分辨图像和多光谱遥感自身的特点, 提出一种非下采样 Contourlet (NSCT) 域的图像融合新策略. NSCT 具有好的多分辨、移不变和多方向等特性, 能对图像中的边缘和围线信息给出渐近最优表示. 为了更好地保持空间分辨率和颜色分量, 引入基于 LHS 变换的亮度成分叠加策略. 实验结果表明: 本文提出的融合方法在提高空间分辨率的同时较好地保持了光谱信息. 与传统的 PCA 方法、基于 IHS 的融合方法、基于小波加权的融合方法, 以及同样采用本文的融合策略、分别基于小波变换和基于 Contourlet 变换的融合策略相比较, 本文方法在视觉效果和客观衡量指标两方面都有所改善.     

基于PST相位约束和稀疏表示的MS和PAN影像融合算法

在基于多光谱(MS)影像和全色(PAN)遥感影像融合中,提高融合影像质量的一个关键问题是如何有效提取PAN影像的纹理特征信息,并有针对性地对MS影像进行信息注入.因此,文中提出基于相位拉伸变换(PST)相位约束的MS和PAN影像稀疏融合算法.首先对MS和PAN影像进行高斯滤波.对于中低频信息,基于PST相位差对影像中边缘和纹理区域的敏感性,通过高频信息PST的相位差获得融合权重约束.对于高频信息,通过学习PAN影像的高频信息获得训练字典,并利用字典对MS和PAN影像的高频信息进行稀疏表示和融合,提高融合高频信息的准确度.算法在一定程度上克服传统融合方法对边缘纹理区域融合效果较差和光谱信息扭曲等现象,取得更好的融合效果.大量仿真实验验证算法的有效性.     

基于改进的NSCT红外可见光图像融合算法

针对在红外可见光图像融合过程中目标细节信息容易丢失的问题,提出一种使用非下采样轮廓波变换（NSCT）和主成分分析法（PCA）相结合的图像融合算法。首先应用NSCT将源图像分解分别得到低频和高频的子带图像。在低频子带系数中,由于PCA能够突出图像的主要信息,所以选用主成分分析法融合规则。高频子带中,相对来说较高层次系数表达的是源图像中最为细节的信息,可选用绝对最大值法融合规则,而相比之下低层次系数代表了较为粗糙的信息,可选用绝对最大值与区域标准差融合规则。从实验结果可以得出,在红外可见光图像目标信息和细节信息融合效果上该算法优于其他算法,有更好的图像视觉效果。     

TM和SAR影像主分量变换融合法

探讨基于主分量变换法融合TM和SAR影像,采用包含广东省三水城的TM和航空SAR影像融合结果表明:主分量变换融合法不仅能提高多光谱影像的信息量和空间分解力,而且很大程度上保留了原多光谱影像的光谱特征。因此多光谱影像经采用主分量变换融合法与SAR融合后,在量测和解译能力上都有提高。与HIS变换融合法相比,主分量变换融合法对光谱特征的扭曲程度没有HIS融合法严重,因此它必将在实际中得到更广泛的应用。     

Combining the spectral PCA and spatial PCA fusion methods by an optimal filter

High correlation among the neighboring pixels, both spectrally and spatially in a multispectral image makes it indispensable to use relevant data transformation approaches, before performing image fusion. The principal component analysis (PCA) method has been a popular choice for the spectral transformation. To propose a new consistent data transformation method in spatial domain, this paper applies the PCA transform to the spatial information of the neighboring pixels. Owing to the fact that the coefficients of PCA are obtained from statistical properties of data, they are adaptive and robust. Then, a new hybrid algorithm is proposed combining the spectral PCA and spatial PCA methods, by an optimal filter to make the synthesized result more similar to what the corresponding multisensors would observe at the high-resolution level. The evaluation of the pan-sharpened images, using global validation indexes, reveals that the proposed approach improves the fusion quality compared with six state of the art fusion methods.     

基于超分辨率的多聚焦图像融合算法研究

针对传统小波变换在图像融合过程中出现边缘模糊、图像失真等问题,提出了一种基于超分辨率的多聚焦图像融合算法。对所有的源图像进行了双三次插值的单帧超分辨率处理,增强源图像对比度等细节信息,采用的源图像为分别进行左右聚焦处理的同一场景中的两幅图像。对这些高分辨率源图像实现了平稳小波变换（SWT）,并将源图像划分为四个子带。针对这些子带所包含源图像细节信息混乱、结构信息冗余等问题,采用了主成分分析（PCA）,分别选取源图像各子带的最大信噪比进行图像融合。利用逆平稳小波变换（ISWT）对融合子带进行重构,得到高质量融合图像。为了评定融合后图像的质量,选择了无参考图像和全参考图像的两种度量方法来检测融合后的图像质量。经实验结果表明,提出的算法克服了传统小波变换算法在图像融合上的缺点,具有边缘清晰、视觉感知好、清晰度好、失真小等优点。     

Color Medical Imaging Fusion Based on Principle Component Analysis and F-Transform

In last years, various medical image fusion algorithms have been proposed to fuse medical image. But, most of them focus on fusing grayscale images. This paper proposes a qualified algorithm for the fusion of multimodal color medical images. The technique of F-transforms has mainly been employed as a fusion technique for images obtained from equal or different modalities. The restriction of fused color mixing RGB, substitution method is resolved by incorporating F-transform and color mixing RGB. The proposed method significantly outperforms the traditional methods in terms of both visual quality and objective evaluation, with improved contrast and overall intensity. The proposed method provides better visual information than the gray ones and more adaptable to human vision. Additional, PCA is functional on the two-level decomposition to maximize the spatial resolution. Experimental evaluation demonstrates that the proposed algorithm qualitatively outperforms many existing state-of-the-art multimodal image fusion algorithms.     

通过直方图中轴化提高融合图像光谱保真度

针对遥感图像融合过程中光谱失真问题,提出一种基于直方图中轴化策略的图像融合算法。首先,将多光谱图像进行IHS变换;然后,采用直方图中轴化策略调整多光谱图像强度分量图像和全色图像的像素直方图,使之趋于一致;最后,进行IHS反变换获得高质量的彩色图像。理论分析和实验结果表明,该算法不仅可以较好地抑制融合图像光谱失真,同时也能有效保留融合图像的空间分辨率,算法步骤简单、容易实现;与四种传统融合算法（IHS变换、主成分分析（PCA）法、小波变换（WT）法、Brovey）相比,该算法生成的融合图像具有良好的视觉效果,特别是在峰值信噪比（PSNR）、光谱扭曲度和信息熵等客观评价指标中明显优于对比算法。基于直方图中轴化策略融合的遥感图像光谱失真度小、空间信息保持度高。