图像融合研究综述

分析了图像融合结构,评述了国内外图像融合方法,并对目前研究热点的彩色图像融合进行了论述,阐明了图像融合评估的研究现状。最后,说明了图像融合发展的影响因素,并指出了图像融合发展的3个主要方向与研究热点。     

A survey on region based image fusion methods

Image fusion has been emerging as an important area of research. It has attracted many applications such as surveillance, photography, medical diagnosis, etc. Image fusion techniques are developed at three levels: pixel, feature and decision. Region based image fusion is one of the methods of feature level. It possesses certain advantages – less sensitive to noise, more robust and avoids misregistration. This paper presents a review of region based fusion approaches. A first hand classification of region based fusion methods is carried out. A comprehensive list of objective fusion evaluation metrics is highlighted to compare the existing methods. A detailed analysis is carried out and results are presented in tabular form. This may attract researchers to further explore the research in this direction.     

Multi sensor image fusion for surveillance applications using hybrid image fusion algorithm

Multimedia Tools and Applications - Image fusion is the process of integrating several source images into a single image that provides more reliable information along with reduced redundancy. In...     

Multi-focus image fusion based on smooth and iteratively restore filter

Multimedia Tools and Applications - Image fusion is the process of combining several images with different focus settings into a completely in-focus image. However, many state-of-the-art fusion...     

High-resolution satellite image fusion using regression kriging

Image fusion is an important component of digital image processing and quantitative image analysis. Image fusion is the technique of integrating and merging information from different remote sensors to achieve refined or improved data. A number of fusion algorithms have been developed in the past two decades, and most of these methods are efficient for applications especially for same-sensor and single-date images. However, colour distortion is a common problem for multi-sensor or multi-date image fusion. In this study, a new image fusion method of regression kriging is presented. Regression kriging takes consideration of correlation between response variable (i.e., the image to be fused) and predictor variables (i.e., the image with finer spatial resolutions), spatial autocorrelation among pixels in the predictor images, and the unbiased estimation with minimized variance. Regression kriging is applied to fuse multi-temporal (e.g., Ikonos, QuickBird, and OrbView-3) images. The significant properties of image fusion using regression kriging are spectral preservation and relatively simple procedures. The qualitative assessments indicate that there is no apparent colour distortion in the fused images that coincides with the quantitative checks, which show that the fused images are highly correlated with the initial data and the per-pixel differences are too small to be considered as significant errors. Besides a basic comparison of image fusion between a wavelet based approach and regression kriging, general comparisons with other published fusion algorithms indicate that regression kriging is comparable with other sophisticated techniques for multi-sensor and multi-date image fusion.     

一种基于小波变换的多分辨图像融合算法

图像的融合过程是将两个或更多的图像结合成一个图像,融合图像保留了每一个图像的重要特征。图像融合技术能够提高图像质量和数据的实用性。主要描述了图像融合的两个课题：（1）融合算法;（2）质量评估。介绍的图像融合算法是先对原图像进行多分辨小波变换,再把输入图像的小波变换系数通过一定的融合规则进行选择,然后经过逆小波变换得到新的图像,对图像融合的质量评估也进行了讨论。     

一种基于小波变换的多尺度图像融合方法

近年来图像的数据融合技术在图像处理领域中得到了广泛的重视和应用。如何对同一目标的多源遥感图像数据进行有效的融合，最大限度地利用多源遥感数据中的有用信息，提高系统的正确识别、判断和决策能力，是遥感数据融合研究的重要内容之一。在小波变换金字塔结构的基础上，提出了一种基于小波变换的多尺度图像融合方法，对热红外与可见光图像进行了融合处理。实验结果表明，该融合方法十分有效，获得的融合图像更符合人们的视觉特性，更有利于机器视觉。     

一种基于改进的概率图像融合方法

图像融合技术是近几年来研究的一个新领域,针对不同传感器生成图像的物理特性,提出了一种使用改进的概率方法对图像进行融合。该方法能有效地降低传感器噪声在图像融合过程中的干扰程度,最大程度地提供融合过程中更丰富、更精确、更可靠的信息,以便于图像的进一步分析、处理与理解。     

Wavelet-based self-supervised learning for multi-scene image fusion

Neural Computing and Applications - Image fusion can provide more comprehensive information by integrating images from different sensors. So far, intensive research has been devoted to utilizing...     

Image fusion in open-architecture PACS-environment

Multimodal digital imaging is common in many fields of diagnosis and therapy planning - there is great interest in matching globally, fusing or registering data from the same part of the body. In practice, there are still difficulties in customizing image fusion in hospitals. Efficient routine use of image fusion requires, among others, an image management infrastructure - a picture archiving and communication system (PACS) - to provide storage of image data in a standard digital format, intelligent image management and fault-tolerant high-speed image networking. In order to customize image fusion, advances in both fusion software and hardware are also needed. The algorithms should be automatic, fast and accurate enough. Registration of multimodal data also creates a need for different display techniques and user-friendly interfaces. Image fusion has been impractical and too tedious to be performed in routine work, but in the future, fused images will be used in clinical practice - even in teleradiological consultation.     

A wavelet-based image fusion tutorial

The objective of image fusion is to combine information from multiple images of the same scene. The result of image fusion is a new image which is more suitable for human and machine perception or further image-processing tasks such as segmentation, feature extraction and object recognition. Different fusion methods have been proposed in literature, including multiresolution analysis. This paper is an image fusion tutorial based on wavelet decomposition, i.e. a multiresolution image fusion approach. We can fuse images with the same or different resolution level, i.e. range sensing, visual CCD, infrared, thermal or medical. The tutorial performs a synthesis between the multiscale-decomposition-based image approach (Proc. IEEE 87 (8) (1999) 1315), the ARSIS concept (Photogramm. Eng. Remote Sensing 66 (1) (2000) 49) and a multisensor scheme (Graphical Models Image Process. 57 (3) (1995) 235). Some image fusion examples illustrate the proposed fusion approach. A comparative analysis is carried out against classical existing strategies, including those of multiresolution.     

利用互带能量和小波变换进行图像融合

图像融合规则对融合的质量起着主要的作用,但目前大部分基于小波变换的融合规则中,都是在输入图像的单一分解层上独立进行的。提出了一种基于互带能量和小波变换的图像融合方法。该方法在融合输入图像的细节信息时,同时考虑了输入图像的多个分解层的小波系数。实验结果表明,无论是依据均方根误差、信噪比、灰度平均误差等客观评价标准,还是视觉的主观评价,所提出的融合方法都能取得较好的性能。     

基于提升格式小波变换的医学图像融合技术

图像融和是对取自不同时间、不同传感器或不同视角的同一场景的两幅图像或者多幅图像进行信息融合的过程,主要用于消除来自不同传感器图像中目标的位置差异。在分析常见算法的基础上,提出了一种新医学图像融合算法,新算法以提升格式小波变换为基础,利用图像多级小波分解后近似分量的轮廓相似性进行图像的快速融合。实验结果表明,新方法高效精确。     

免疫粒子群优化算法在图像融合中的应用

提出了一种基于图像分块的小波多聚焦图像融合方法,并将免疫粒子群优化搜索策略应用于多聚焦图像融合子块寻优中。将图像子块作为粒子,以寻求最优组合分块形成的融合图像。利用两种评价参量,即信息熵和交叉熵进行不同图像融合方法的分析及效果评价,实验结果表明,其融合性能优于对图像只进行分块而不作小波分解的融合方法和只作小波分解而不进行分块的融合方法,该方法既能消除块痕迹,又能节约运算量,取得了很好的融合效果。与标准粒子群相比,免疫粒子群的收敛性能和达优率更好。     

Image fusion with morphological component analysis

Image fusion can produce a single image that describes the scene better than the individual source image. One of the keys to image fusion algorithm is how to effectively and completely represent the source images. Morphological component analysis (MCA) believes that an image contains structures with different spatial morphologies and can be accordingly modeled as a superposition of cartoon and texture components, and that the sparse representations of these components can be obtained by some specific decomposition algorithms which exploit the structured dictionary. Compared with the traditional multiscale decomposition, which has been successfully applied to pixel-level image fusion, MCA employs the morphological diversity of an image and provides more complete representation for an image. Taking advantage of this property, we propose a multi-component fusion method for multi-source images in this paper. In our method, source images are separated into cartoon and texture components, and essential fusion takes place on the representation coefficients of these two components. Our fusion scheme is verified on three kinds of images and compared with six single-component fusion methods. According to the visual perceptions and objective evaluations on the fused results, our method can produce better fused images in our experiments, compared with other single-component fusion methods.     

Multi-scale fidelity measure for image fusion quality assessment

Image fusion is considered an effective enhancing methodology widely included in high-quality imaging systems. Nevertheless, like other enhancing techniques, output quality assessment is made within small sample subjective evaluation studies which are very limited in predicting the human-perceived quality of general image fusion outputs. Simple, blind, universal and perceptual-like methods for assessing composite image quality are still a challenge, partially solved only in particular applications. In this paper, we propose a fidelity measure, called MS-Q_W with two major characteristics related to natural image statistics framework: A multi-scale computation and a structural similarity score. In our experiments, we correlate the scores of our measure with subjective ratings and state of the art measures included in the 2015 Waterloo IVC multi-exposure fusion (MEF) image database. We also use the measure to rank correctly the classical general fusion methods included in the Image Fusion Toolbox for medical, infra-red and multi-focus image examples. Moreover, we study the scores variability and statistical discrimination power with the TNO night vision database using the Friedman test. Finally, we define a new leave one out procedure based on our fidelity measure that selects the best subset of images (within a collection of distorted and unregistered cell phone type images) that provides a defect-free composite output. We exemplify the procedure with the fusion of a collection of images from Latour and Van Dongen paintings suffering from glass highlights and speckle noise, among other artifacts. The proposed multiscale quality measure MS-Q_W demonstrates improvement over the previous single-scale similarity measures towards a fidelity assessment between quantitative image fusion quality metrics and human perceptual qualitative scores.     

基于单尺度脊波变换的图像融合

图像融合在一定程度上可以理解为对多个二维函数的奇异信息进行分析、提取、综合的过程。单尺度脊波变换的函数逼近性能要优于小波变换，因此，对应于图像中的边缘及角点的单尺度脊波系数的能量要更加集中。分析了基于变换域的图像融合算法的性能与所用变换的函数逼近性能的关系，提出了一种新的基于单尺度脊波变换的图像融合算法。在多种融合规则下，将该方法与基于Laplacian塔型变换、小波变换等其他图像融合方法进行了比较，实验结果表明，基于单尺度脊波变换的融合方法具有更好的融合效果。     

一种基于提升小波的图像融合方法

文章探讨了一种新的基于提升小波变换的图像融合算法。该算法能够有效地解决目前常用的多尺度分解方法所存在的运算速度慢、对内存的需求量大、不适于实时应用的局限性。同时，该算法可以最大限度地保留待融合图像的信息，提高了待融合图像的清晰度。实验结果表明，该算法在执行时间和融合后的图像质量上都优于传统的方法。     

Image fusion using intuitionistic fuzzy sets

Image fusion is the process of combining one or more images which are obtained from different environment into a single image which is more useful for further image processing tasks. Image registration and image fusion are of great importance in defence and civilian sectors, particularly for recognizing a ground/air force vehicle and medical imaging. In this paper a new way is drawn to fuse two or more images by using maximum, minimum operations in intuitionistic fuzzy sets (IFSs). IFSs are more suitable for image processing since every digital image have lot of uncertainties. In processing phase, images are reformed into intuitionistic fuzzy images (IFIs). Entropy is employed to obtain the optimum value of the parameter in membership and non-membership function. Then the resulting IFIs are disintegrated into image blocks and the corresponding blocks of the images are reunioned by finding the count of blackness and whiteness of the blocks. This paper evaluates the performance of simple averaging (AVG), principal component analysis (PCA), discrete wavelet transform (DWT), stationary wavelet transform (SWT), dual tree complex wavelet transform (DTCWT), multi-resolution singular value decomposition (MSVD), nonsubsampled contourlet transform (NSCT) and IFS (proposed method) in terms of various performance measure. The experimental and comparison results show that luminance and contrast is of great importance for image processing and prove that the proposed method is better than all other methods.     

基于能量最小化的图像融合

图像融合是指联合两个或两个以上的图像通过某种算法得到一幅更高质量的新图像.提出了一种融合全色图像和光谱图像的方法即能量最小化方法,能量主要由两个部分组成.第1部分保证了相关细节信息的注入.第2部分保持了多光谱图像的低频信息.另外,能量还可以包含高分辨率光谱图像的先验知识和其它一些约束条件.