共查询到20条相似文献,搜索用时 15 毫秒
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Zhang Yifan He Mingyi 《电子科学学刊(英文版)》2007,24(2):218-224
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. 相似文献
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基于小波变换的加权图像融合方法 总被引:4,自引:2,他引:2
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。 相似文献
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采用提升方向波变换的异源图像融合新算法 总被引:5,自引:0,他引:5
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. 相似文献
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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。 相似文献
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Cheng Yinglei Zhao Rongchun Hu Fuyuan Li Ying 《电子科学学刊(英文版)》2006,23(2):314-317
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. 相似文献
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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). 相似文献
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《Journal of Visual Communication and Image Representation》2014,25(5):951-962
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. 相似文献
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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. 相似文献
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可见光与红外图像增强融合算法研究 总被引:1,自引:0,他引:1
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。 相似文献
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Bin Tian Robert J. Sclabassi Jennting T. Hsu Qiang Liu Ching Chung Li Mingui Sun 《电子科学学刊(英文版)》2007,24(5):642-648
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. 相似文献