基于小波变换的不同融合规则的图像融合研究

图像融合是一种重要的增强图像信息的方法。提出了一种基于多尺度分解的像素图像融合方法。利用小波变换按照不同融合规则及融合算子构造融合图像对应的小波系数。通过对可见光图像与红外图像进行融合的实验比较表明，以像素的局部能量为准则的融合效果更好，既可避免传统融合规则的信息损失，又提高了融合图像的空间分辨率和清晰度，融合图像更符合人的视觉特性，也有利于机器视觉。     

Multi-spectral and hyperspectral image fusion using 3-D wavelet transform

Image fusion is performed between one band of multi-spectral image and two bands of hyperspectral image to produce fused image with the same spatial resolution as source multi-spectral image and the same spectral resolution as source hyperspeetral image. According to the characteristics and 3-Dimensional （3-D） feature analysis of multi-spectral and hyperspectral image data volume, the new fusion approach using 3-D wavelet based method is proposed. This approach is composed of four major procedures： Spatial and spectral resampling, 3-D wavelet transform, wavelet coefficient integration and 3-D inverse wavelet transform. Especially, a novel method, Ratio Image Based Spectral Resampling （RIBSR）method, is proposed to accomplish data resampling in spectral domain by utilizing the property of ratio image. And a new fusion rule, Average and Substitution （A＆S） rule, is employed as the fusion rule to accomplish wavelet coefficient integration. Experimental results illustrate that the fusion approach using 3-D wavelet transform can utilize both spatial and spectral characteristics of source images more adequately and produce fused image with higher quality and fewer artifacts than fusion approach using 2-D wavelet transform. It is also revealed that RIBSR method is capable of interpolating the missing data more effectively and correctly, and A＆S rule can integrate coefficients of source images in 3-D wavelet domain to preserve both spatial and spectral features of source images more properly.     

基于小波变换的红外图像融合技术研究

图像融合的目的是把来自多传感器数据的互补信息合并形成一幅新的图像,以便更好地进行监视和侦察之类的视觉感知。文中介绍了小波变换及其Mallat快速算法,提出了基于小波变换的图像融合方法,并分析了可见光与红外图像的融合结果,结果表明,小波变换融合图像包含了所有传感器的相关信息,其性能优于高斯－拉普拉斯金字塔结构算法,这一原理也可以应用到两幅以上的图像上,无论这些旬否在同一光谱波段内。     

基于小波多分辨分析的图像融合方法

图像融合的目的是把来自多传感器数据的互补信息合并形成一幅新的图像,以便更好地进行监视和侦察之类的视觉感知[1,2]。在阐述了小波变换多分辨分析的基本原理及其特点的基础上,提出了基于小波多分辨分析的图像融合的新方法。实验结果表明,该方法效果良好,可广泛用于各研究领域。     

基于小波变换的加权图像融合方法

提出了一种基于小波变换的多光谱图像与高空间分辨率图像融合方法。该方法通过局部方差准则利用加权平均将高空间分辨率图像和多光谱图像经小波分解的低频分量进行融合,然后将融合的低频分量和高空间分辨率图像的细节分量结合进行小波反演变换得到融合图像。通过和WT方法的融合结果进行对比评价,表明了该方法在提高多光谱图像的空间细节表现能力和保持光谱信息上都有很好的效果。     

采用提升方向波变换的异源图像融合新算法

方向波(Directionlet)变换是一种基于格子的歪斜小波变换,与标准二维小波变换相比,它具有多方向性和各向异性,能够更好地描述图像的特征.针对异源图像融合这一研究热点,提出了一种新的基于方向波变换的图像融合方法,并采用提升算法有效地解决了该变换方法的运算速度问题.首先,对已配准的两幅图像分别沿变换和队列方向进行次数不等的提升变换,得到具有各向异性的子图;然后,采用低频子图直接平均融合,高频部分选择具有较强各向异性信息分量的方法得到融合图像的所有方向波变换系数;最后,经过反变换得到融合图像.实验结果表明:该算法的融合效果和运算速度都优于标准小波变换和其他的二代小波变换.     

基于小波变换的图像融合方法

提出了一种基于小波变换的改进图像融合方法。对小波分解后的高频分量使用图像区域内像素的最大绝对值作为该区域中心像素的活性测度以得到融合图像的高频分量,对分解后的低频分量通过度量其图像块的质量来确定融合图像的低频分量。实验结果表明,该方法得到的融合图像清晰度和对比度都较好,是一种有效的图像融合算法。     

基于小波变换多尺度积的图像融合算法

图像融合是图像处理中的关键技术之一。它在军事和民用图像处理领域获得了广泛的应用。提出了一种新的基于小波变换多尺度积的图像融合算法,小波变换多尺度积具有放大信号边缘特征和降低信号噪声的特点,有利于在融合图像中保持图像的细节特征。利用统计分析的评判准则,如熵、标准偏差评价图像的融合效果。实验结果表明该方法提高了图像的熵和标准偏差。在保留原图像信息的情况下增强了融合图像的细节信息。     

基于小波变换的双色红外图像融合检测方法

针对复杂背景中/长波红外图像的特点,提出了一种基于小波变换的中/长波红外图像融合检测方法。首先,对原始中/长波红外图像分别进行小波变换,提取出两幅图像的低频和高频信息;然后,分别对图像的低频和高频信息采用不同的融合准则进行融合,并用小波逆变换重建成融合图像,自适应选取阈值,获得最终的目标检测结果。在实验中,采用相同的方法分别用融合图像、单波段红外图像进行目标检测,实验结果证明,用文中提出的融合检测算法优于仅用长波或中波红外图像的目标检测。     

基于小波的全局多源遥感图像信息融合方法

提出的图像信息融合方法用于解决传统方法在空间分辨率等方面的不足。原始图像经过小波变换，分解成子图像，再进行分块处理，高、低分辨率图像根据全局方差准则分别计算融合的权值系数，进行图像融合重建，然后，对图像采用全局法进行信息融合，实验表明，方法切实可行，能使低分辨率图像接近于高分辨率图像。     

A novel algorithm of multi-sensor image fusion based on wavelet packet transform

In order to enhance the image information from multi-sensor and to improve the abilities of the information analysis and the feature extraction, this letter proposed a new fusion approach in pixel level by means of the Wavelet Packet Transform （WPT）. The WPT is able to decompose an image into low frequency band and high frequency band in higher scale. It offers a more precise method for image analysis than Wavelet Transform （WT）. Firstly, the proposed approach employs HIS （Hue, Intensity, Saturation） transform to obtain the intensity component of CBERS （China-Brazil Earth Resource Satellite） multi-spectral image. Then WPT transform is employed to decompose the intensity component and SPOT （Systeme Pour I＇Observation de la Therre ） image into low frequency band and high frequency band in three levels. Next, two high frequency coefficients and low frequency coefficients of the images are combined by linear weighting strategies. Finally, the fused image is obtained with inverse WPT and inverse HIS. The results show the new approach can fuse details of input image successfully, and thereby can obtain a more satisfactory result than that of HM （Histogram Matched）-based fusion algorithm and WT-based fusion approach.     

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).     

基于小波分析的图像融合评述

图像融合是将同一场景的多幅图像的互补信息合并成一幅新图像,以便更好地对场景进行监视和侦察。小波分析具有多分辨等特点,可以有效地将特征明显、分辨率高的图像融合在一起,得到比任何一幅源图像效果都好的图像。阐述了基于小波分析的图像融合的基本原理、方法及优点。比较了各种图像融合规则和融合算子的特点。介绍了基于小波包图像融合、小波框架图像融合和多小波图像融合方法。     

A novel multi-focus image fusion algorithm based on random walks

In multi-focus image fusion, the aim is to create a single image where the whole scene is focused by fusing multiple images captured with different focus distances. The fused image has greater depth of field than each of the input images. In this paper, we present a new method for multi-focus image fusion via random walks on graphs. The proposed method first evaluates the focus areas in a local sense and identifies nodes corresponding to consistency of nodes in a global sense. Several popular feature sets based on focus measure and color consistency are evaluated and employed to create a fully connected graph to model the global and local characteristics, respectively, of the random walks. The behavior of random walks on the graph is utilized to compute the weighting factor for each of the shallow depth-of-field input image. Experimental results show that the proposed method outperforms many state-of-the-art techniques in both subjective and objective image quality measures.     

基于边缘的小波图像融合算法

提出一种基于边缘的小波图像融合算法,针对小波分解的不同频率域,分别对高频系数和低频系数设计不同的融合规则。采用小波系数局部模极大和定义的局部信号强度比相结合的方法融合高频系数;对低频系数采用一种基于边缘的方法,在两幅原图像中选择最有可能是边缘的点加以保留。实验结果表明:采用此融合规则得到的融合图像具有良好的融合效果。     

Multi-focus image fusion using HOSVD and edge intensity

The purpose of multi-focus image fusion is to integrate the partially focused images into one single image which is focused everywhere. To achieve this purpose, higher order singular value decomposition (HOSVD) and edge intensity (EDI) based multi-focus image fusion method is proposed. The main characteristics of the proposed method includes: 1. an effective and robust sharpness measure based on edge intensity is presented; 2. considering the fact that HOSVD is an effective data-driven decomposition technique and shows the outstanding ability in the representation of high-dimensional data, it is used to decompose multi-focus images; and 3. a multi-strategy fusion rule based on sigmoid function is used to fuse the decomposition coefficients. Furthermore, several experiments are conducted to verify the superiority of the proposed fusion framework in terms of visual and statistical analyses.     

基于小波变换的多规则图像融合方法

针对红外和可见光图像各自特征,提出了基于小波变换多分辨率的不同规则图像融合方法.首先对原图像进行多级小波分解,产生多频带小波系数,利用像素的局部区域方差、平均梯度及掩膜滤波等方法对不同频带小波系数计算融合权重,最后将小波融合系数逆变换实现图像的融合处理.实验结果表明,融合图像特征显著,易于观察.     

可见光与红外图像增强融合算法研究

提出了自适应图像增强算法，用于可见光和红外图像的融合。首先对输入的可见光图像和红外图像进行自适应增强，然后采用基于图像空间能量窗及归一化互相关测度构造融合图像，利用图像的信息熵评估算法的融合效果，最后给出了一组可见光和红外图像融合的试验结果，表明该算法十分有效，融合图像有丰富的互补信息，有利于人眼观察和目标识别。     

一种基于整数小波变换的遥感影像融合算法

提出了一种IHS变换和整数小波变换相结合的遥感影像融合算法(IHS-IWT),并根据待融合图像的特点,选取了有效的融合策略.首先对中巴卫星多光谱影像进行IHS变换,然后对得到的亮度分量和Landsat ETM 高分辨率全色影像进行整数小波融合,最后通过整数小波逆变换得到融合的图像.实验结果表明,与IHS变换等方法比较,该方法在较好的保留光谱信息的同时,空间细节信息也得到了增强,并且具有较快的处理速度.     

A wavelet transform based POCS Superresolution algorithm

Research interest in multi-frame Superresolution has risen substantially in recent years.This paper presents a modified Projection Onto Convex Set (POCS) superresolution method based on wavelet transform.The method analyzes the image formation model from wavelet multiresolution analysis point of view and defines an closed convex set and its corresponding projection based on wavelet transform.An iterative procedure is utilized to reduce the estimated errors of the result image,and this guarantees the estimated image to lay in the intersection of different convex sets,thus produces a high resolution image with a reduced error.The effectiveness of the algorithm is demonstrated by experimental results.