基于NSST图像特征的多聚焦图像融合方法

针对广泛存在于多聚焦图像融合方法中的局部图像细节不清晰的现状,提出一种基于非下采样剪切波变换(NSST)图像特征的多聚焦图像融合方法。利用NSST对待融合源图像进行多尺度、多方向稀疏分解,分别获取低频和一系列高频子带图像。通过空间频率和局部能量确定融合后的低频子带系数,利用边缘检测算子直接获取高频子带图像中的细节和边缘信息,并采取NSST反变换得到最终融合结果图像。仿真实验结果表明,同现有的几种经典算法相比,本文提出的方法获得的结果图像拥有更清晰的视觉效果、更理想的客观指标效果以及更高的算法运行效率。     

基于对比度的多分辨图像融合

本文给出一种基于对比度的多分辨图像融合算法.首先,利用小波变换得到待融合图像的多分辨分析,同时得到图像的多分辨对比度序列;然后以对比度为判据,在图像的多分辨分析的相应各级上进行融合,得到融合图像的多分辨分析;最后,利用小波逆变换重构融合图像.算法使用同一场景可见光图像和红外图像进行了验证,结果表明,融合图像完好地显示了源图像各自的信息.     

Objective image fusion performance measure

A measure for objectively assessing the pixel level fusion performance is defined. The proposed metric reflects the quality of visual information obtained from the fusion of input images and can be used to compare the performance of different image fusion algorithms. Experimental results clearly indicate that this metric is perceptually meaningful     

Gradient-based multiresolution image fusion

A novel approach to multiresolution signal-level image fusion is presented for accurately transferring visual information from any number of input image signals, into a single fused image without loss of information or the introduction of distortion. The proposed system uses a "fuse-then-decompose" technique realized through a novel, fusion/decomposition system architecture. In particular, information fusion is performed on a multiresolution gradient map representation domain of image signal information. At each resolution, input images are represented as gradient maps and combined to produce new, fused gradient maps. Fused gradient map signals are processed, using gradient filters derived from high-pass quadrature mirror filters to yield a fused multiresolution pyramid representation. The fused output image is obtained by applying, on the fused pyramid, a reconstruction process that is analogous to that of conventional discrete wavelet transform. This new gradient fusion significantly reduces the amount of distortion artefacts and the loss of contrast information usually observed in fused images obtained from conventional multiresolution fusion schemes. This is because fusion in the gradient map domain significantly improves the reliability of the feature selection and information fusion processes. Fusion performance is evaluated through informal visual inspection and subjective psychometric preference tests, as well as objective fusion performance measurements. Results clearly demonstrate the superiority of this new approach when compared to conventional fusion systems.     

基于快速整数提升小波变换的多幅图像融合

在分析已有多分辨率图像融合方法的基础上,针对多幅图像融合模型的选择问题,提出了一种基于快速整数提升小波变换的多幅图像融合新算法。首先采用整数提升小波变换将多幅源图像分解到不同尺度、方向频带范围内,然后根据图像提升小波变换后不同子带的特点分别采用了2种新的高、低频融合策略,最后通过整数提升小波逆变换得到融合图像。对多幅源图像进行了融合实验,并对融合结果进行了主观和客观的评价。实验结果表明,该算法不仅适合多幅图像的实时快速融合,而且可以获得视觉效果较佳、细节更为丰富的融合图像。     

基于PCNN和BWT的图像融合算法

提出了一种新型图像融合算法.首先,对待融合的两幅图像进行双正交小波分解得到两组多尺度图像;接着,取其中任意一组作为主PCNN的输入、另一组相应的图像作为从PCNN的输入,在每次迭代时,经并行PCNN点火后,得到一系列多尺度融合图像;然后,对它们进行双正交小波重构得到每次迭代的融合结果,并计算每次迭代结果的信息熵,取信息熵值最大的融合图像作为最终结果.大量的实验以及与其他融合算法的比较分析,表明了本文算法的有效性和优越性.     

Fusion algorithm for multisensor images based on discrete multiwavelet transform

The authors review the notion of multiwavelets and describe the use of the discrete multiwavelet transform (DMWT) in image fusion processing. Multiwavelets are extensions from scalar wavelets, and have several advantages in comparison with scalar wavelets. Multiwavelet analysis can offer more precise image analysis than wavelet multiresolution analysis. A novel fusion algorithm is presented for multisensor images based on the discrete multiwavelet transform that can be performed at pixel level. After the registering of source images, a pyramid for each source image can be obtained by applying decomposition with multiwavelets in each level. The multiwavelet decomposition coefficients of the input images are appropriately merged and a new fused image is obtained by reconstructing the fused multiwavelet coefficients. This image fusion algorithm may be used to combine images from multisensors to obtain a single composite with extended information content. The results of experiments indicate that this image fusion algorithm can provide a more satisfactory fusion outcome.     

Multimodal medical image fusion by cloud model theory

Image fusion can provide more extensive information since it combines two or more different images. Cloud model is a recently proposed theory in artificial intelligence and has the advantage of taking the randomness and fuzziness into account. In this paper, we introduce a novel multimodal medical image fusion method by cloud model theory. The proposed method fits the histograms of input images using the high-order spline function firstly and then divides intervals in line with the valley point of the fitted curve. On this basis, cloud models are generated adaptively through the reverse cloud generator. Finally, cloud reasoning rules are designed to achieve the fused image. Experimental results demonstrate that the fused images by proposed method show more image details and lesion regions than existing methods. The objective image quality assessment metrics on the fused images also show the superiority of the proposed method.     

Image fusion metric based on mutual information and Tsallis entropy

A novel image fusion performance metric using mutual information is proposed. The metric is based on Tsallis entropy, which is a one-parameter generalisation of Shannon entropy. Experimental results have confirmed that the proposed metric outperforms the standard MI metric by correlating better with the subjective quality of fused images.     

基于Contourlet变换和IPCNN的融合算法及其在可见光与红外线图像融合中的应用

针对多传感器图像融合这一图像处理领域中的研究热点问题,提出了一种基于Contourlet变换和IPCNN的融合方法.该融合方法首先利用Contourlet对输入图像进行多尺度、多方向稀疏分解,准确地捕获图像中的二维或高维奇异信息,然后在Contourlet域充分利用IPCNN的同步激发特性,进行基于IPCNN的融合策略设计,提高了融合效果.仿真结果表明,该算法具有很好的融合效果.     

压缩感知在多源图像融合中的应用

针对全采样传统图像融合方法中计算量大、时间复杂度高的问题,提出了一种基于压缩感知(CS)理论的多源图像融合模型。为满足一定的稀疏性,将源图像在过完备二维离散余弦变换(DCT)字典上进行稀疏表示,并通过随机观测得到待融合的观测值;在每一图像块上采用基于标准差的方法自适应地计算融合权值,加权合成融合后的观测值,然后利用改进步长的梯度追踪算法求解稀疏系数,得到最终融合图像。实验结果表明：与传统方法相比,提出的融合模型在减少计算量和存储容量的同时,能更好地从源图像中提取信息,获得效果较好的融合图像。     

一种基于目标区域的图像融合新方法

提出了一种基于目标区域的融合方法.将原图像分为目标区域和背景区域,在利用àtrous小波算法分解图像的基础上,分别对目标区域、背景区域以及两区域间的边界部分进行不同的融合处理得到融合后小波系数,最后经过小波逆变换得到融合图像.实验结果表明该方法可获得较理想的融合图像,其融合结果优于基于像素和基于窗口的融合方法.     

基于非下采样Contourlet变换的图像融合算法

针对曲波变换图像融合的不足,提出了一种基于非下采样轮廓波变换（Non—subsampledContourletTransform,NSCT）图像融合方法。首先对已配准待融合图像进行NSCT分解;然后使用相应的融合规则对Contourlet域系数进行融合,得到融合图像的NSCT系数;最后经逆变换重构得到融合图像。通过对不同曝光度图像以及多聚焦图像进行融合实验,仿真结果表明该算法融合图像在主观视觉和客观评价指标上均取得良好效果。     

模态自适应的红外与可见光图像融合

为解决低照度和烟雾等恶劣环境条件下融合图像目标对比度低、噪声较大的问题,提出一种模态自适应的红外与可见光图像融合方法(mode adaptive fusion,MAFusion).首先,在生成器中将红外图像与可见光图像输入自适应加权模块,通过双流交互学习二者差异,得到两种模态对图像融合任务的不同贡献比重;然后,根据各模...     

椭圆方向窗与新轮廓波应用于多聚焦融合算法研究

针对光学显微镜景深扩展中的多聚焦图像融合问题,提出了一种基于方向特性的新轮廓波域多聚焦图像融合算法。该算法首先对图像进行新轮廓波变换(New Contourlet Transform with Sharp Frequency Localization,NCT-SFL),分解得到不同尺度、不同方向的高低频系数,低频系数融合使用算术平均法,高频系数融合分为两步:先采用改进拉普拉斯能量和(Sum Modified Laplacian,SML) 提取特征值;然后定义新的与方向分解一一对应的椭圆方向窗,在确定的椭圆窗参数下,对提取的特征值进行累加并以此为依据对高频系数进行融合,最后通过反新轮廓波变换得到融合图像。在实验部分用定义的新的客观评价指标互结构信息(Mutual Structural Information,MSI)对融合算法进行了评价,结果表明:对多聚焦图像本文所提方法比新轮廓波域方形窗算法MSI提高了2.94%,比Contourlet域方形窗与椭圆窗算法MSI分别提高了10.44%和8.56%。说明本文方法能提取源图像中更多的清晰信息到融合图像,是一种有效的景深扩展手段。      

A fuzzy convolutional neural network for enhancing multi-focus image fusion

The images captured by the cameras contain distortions, misclassified pixels, uncertainties and poor contrast. Therefore, the multi-focus image fusion (MFIF) integrates various input image features to produce a single fused image using all its objects in focus. However, it is computationally complex, which leads to inconsistency. Hence, the MFIF method is employed to generate the fused image by integrating the fuzzy sets (FS) and convolutional neural network (CNN) to detect focused and unfocused parts in both source images. It is also compared with other competing six MFIF methods like Neutrosophic set based stationary wavelet transform (NSWT), guided filters, CNN, ensemble CNN, image fusion-based CNN and deep regression pair learning (DRPL). Benchmark datasets validate the superiority of the proposed FCNN method in terms of four non-reference assessment measures having mutual information (1.1678), edge information (0.7281), structural similarity (0.9850) and human perception (0.8020) and two reference metrics such as Peak signal-to-noise ratio (57.23) and root mean square error (1.814).     

基于NSST变换与自适应PCNN的多特征遥感图像融合

针对基于NSCT变换的遥感图像融合算法存在计算复杂度高,细节表现能力不足的问题,本文提出了一种基于NSST变换与自适应PCNN的多特征遥感图像融合算法。首先,利用HSV变换提取MS图像的亮度分量V,并将得到的亮度分量V与PAN图像分别进行NSST变换;其次,对于低频子带,提出了一种基于自适应的PCNN融合规则,将空间频率和区域平均梯度分别作为PCNN的外部激励和链接强度;对于高频子带,采用基于多特征的融合规则;最后,进行逆NSST变换和逆HSV变换得到融合图像。仿真实验表明,该算法与一些经典的融合算法相比不仅可以提高图像融合质量,在视觉效果和客观指标上也都有良好的表现。     

梯度域多曝光图像融合

为解决多曝光图像融合的问题,提出了一种新颖的直接将多曝光图像融合成适合显示的低动态范围图像的方法,并且具备消除运动伪影的能力。使用联合柱状图探究两幅图像之间像素值的对应关系,对每幅图像检测分别得到一幅伪影权值图,用来构造融合时的度量。在梯度域中进行融合,根据多维黎曼流形几何(Riemannian Geometry),从多个输入图像的结构张量中推导得出融合的梯度域,然后使用均值滤波器在多尺度上进行梯度域的非线性修改,通过解泊松方程得到融合图像。最后,还提出了一种新颖的色彩复原算法,用以避免造成色彩偏差。实验表明,该方法能够有效融合含运动物体的多曝光图像,无论哪幅图像被选作参考图像,都能获得高质量的结果图像。该算法同时具有处理的有效性和时间的高效性。     

基于NSCT的多光谱和全色图像的融合

提出了一种基于无下采样Contourlet变换的多光谱和全色图像的融合方法.该方法在对多光谱影像进行IHS变换的基础上,对多光谱的I分量和高分辨率的全色影像分别进行无下采样Contourlet变换(NSCT),然后对分解得到的近似分量以及各层金字塔各方向的细节分量利用本文提出的一定的融合准则分别对近似分量和细节分量进行影像融合,最后通过无下采样Conlourlet逆变换得到新的I分量,与H,S分量一起还原到RGB空间,最终得到融合后的高分辨率多光谱彩色图像.本文采用一组多光谱图像和全色图像数据进行融合实验,其实验融合图像的目视效果和统计指标均优于传统的IHS融合方法、小波融合方法以及Contourlet变换方法.     

基于变换域多合成规则的双色中波红外图像融合

为了综合多个合成规则的优点,取得更好的双色中波红外图像融合效果,提出了基于变换域多合成规则的融合方法.采用支持度变换分别获得两个中波细分波段图像的低频成分图像和支持度图像序列;根据像素值最大法、区域特征最大法、区域特征加权法等合成规则的特点,提出了低频成分图像三个合成规则的组合方法;根据支持度图像的能量最大法、区域特征加权法等合成规则的特点,提出了支持度图像序列两个合成规则的组合方法.与单一合成规则的方法相比,融合后的图像局部标准偏差、局部粗糙度和融合质量参数分别提高了6.77%、4.86%和9.59%,实验结果证明了该融合方法的有效性.