Description: 文中的方法是把图像分块，小波分解得到低频分量、高频分量，然后计算每一块的对比度，把图像块划分为清晰块、模糊块，把清晰块和模糊块相邻的区域定义为边界区域，融合时，直接选取清晰块作为融合后的相应块，对于边界区域，在小波分解的基础上采用基于对比度的像素选取的方法进行处理。-Paper, the method is to image segmentation, wavelet decomposition are low frequency, high frequency components, then calculate the contrast of each piece, the image block is divided into clear blocks, fuzzy block, to clear blocks and fuzzy block is defined as the border region adjacent to area, integration time, a clear block directly select the corresponding block as a fusion, for the border region, the wavelet decomposition on the basis of the pixel-based contrast method selected for processing. Platform: |
Size: 338944 |
Author:许国柱 |
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Description: 数字图象变形是图象处理领域中的一个热点, 具有较大的实用价值. 通过对基于特征变形算法的分析, 在三
维体素数据模型上, 提出了一种基于特征的散乱点三维变形算法. 在算法中首先采用移动平滑插值函数实现对变形
扭曲的拟合, 其次采用融合等方法提高变形的精度和效果, 最后通过试验表明该算法是可行的. 该算法不但可以实现三维变形, 而且可以用于对二维影像的处理.-Image processing digital image deformation is a hot area, with great practical value. By deformation algorithm based on feature analysis of the data in three-dimensional voxel model, a feature-based deformation algorithm for three-dimensional scattered points . In this algorithm, the first move smooth interpolation function is used to achieve the deformation of the fitting twist, followed by fusion method to improve the accuracy and effectiveness of deformation, and finally the experiment show that the algorithm is feasible. The algorithm can not only three-dimensional deformation, and can for two-dimensional image processing. Platform: |
Size: 192512 |
Author:陈东尧 |
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Description: 提出了一种基于人类视觉系统和模糊隶属度函数的小波卫星图像融合新算法,利用小波域的人类视觉系
统经验模型,刻画图像的边缘、纹理及高亮区域, 采用模糊隶属度函数自适应地计算权系数, 在小波域上通过加权
平均实现了图像融合。-Proposed a new algorithm based on human visual system and the fuzzy membership function of the wavelet satellite image fusion using wavelet domain experience in the human visual system model, depicting the image of the edge, texture, and the highlighted area, the use of fuzzy membership function adaptively calculate the weight coefficients in the wavelet domain by the weighted average realized fusion. Platform: |
Size: 256000 |
Author:张凡 |
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Description:
拉格朗日插值 三次样条插值 传感器静态特性及指标 图像测量中镜头的参数及选择 一个基于物联网的测控实例 GPS系统构成及作用 gpS系统的空间坐标系 北斗系统简介 视觉系统硬件组成及功能 人眼可辨别的最小打印点直径的估计 面积测量方法流程及优缺点圆测量方法及椭圆测量难点 光敏电阻应用——声光双控LED实验 气敏（酒精）传感器实验 正六边形面积测量 多光照图像融合 多聚焦图像融合-The Lagrange interpolating cubic spline interpolation measurement sensor static characteristics and indicators image lens parameters and select a composition and functions of the human visual system hardware based on the space coordinate system Beidou system monitoring and control instance GPS system constitutes, and role gpS system of Things About estimated area of measurement of the diameter of the smallest discernible eye print dots process and the advantages and disadvantages of circle measurement method and oval difficult measurement photoresistor application- sound and light double control the LED the experiment gas sensing (alcohol) sensor experiment regular hexagon area measuring multi-light image integration of multi-focus image fusion Platform: |
Size: 2700288 |
Author:郭志 |
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Description: 基于低频融合策略的小波图像融合算法可分为三个细节：高频带的融合、低频融合的对象、低频融合策略的程序代码。其中对最后一部分中一些细节问题要花心思处理，比如区域大小的确定、区域边界与图像边界的关系、区域中心与区域中各点的权值确定、区域中心在原始图像中的具体位置等等。-Fusion strategy based on low-frequency wavelet image fusion algorithm can be divided into three details: integration, low-fusion target, the low-frequency fusion strategy program code the high frequency band. Where some of the details of the last part of the effort is needed to deal with issues such as the relationship between the size of the defined area, the boundary of the image area boundary, regional center and the right value of each point in the area to determine the specific location of regional centers in the original image, etc. . Platform: |
Size: 2048 |
Author:陶陶 |
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Description: Abstract—To overcome the difficulties of sub-band coefficients
selection in multiscale transform domain-based image fusion
and solve the problem of block effects suffered by spatial
domain-based image fusion, this paper presents a novel hybrid
multifocus image fusion method. First, the source multifocus
images are decomposed using the nonsubsampled contourlet
transform (NSCT). The low-frequency sub-band coefficients are
fused by the sum-modified-Laplacian-based local visual contrast,
whereas the high-frequency sub-band coefficients are fused by
the local Log-Gabor energy. The initial fused image is subsequently
reconstructed based on the inverse NSCT with the fused
coefficients. Second, after analyzing the similarity between the
previous fused image and the source images, the initial focus
area detection map is obtained, which is used for achieving the
decision map obtained by employing a mathematical morphology
postprocessing technique. Finally, based on the decision Platform: |
Size: 2480128 |
Author:senthil |
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Description: nsst域内基于区域能量和区域方差取最大值的图像融合算法，简单易懂。主程序高频区域能量取大，低频加权平均，读者可自行修改。-nsst maximum value within an image fusion algorithm based on regional energy and regional variance, easy to understand. Main high-frequency energy to take large area, low-weighted average, readers can modify. Platform: |
Size: 4096 |
Author:晨 |
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Description: 提出一种新的显着性检测方法，通过将区域级显着性估计和像素级显着性预测与CNN（表示为CRPSD）相结合。对于像素级显着性预测，通过修改VGGNet体系结构来执行完全卷积神经网络（称为像素级CNN）以执行多尺度特征学习，基于该学习进行图像到图像预测以完成像素级显着性检测。对于区域级显着性估计，首先设计基于自适应超像素的区域生成技术以将图像分割成区域，基于该区域通过使用CNN模型（称为区域级CNN）来估计区域级显着性。通过使用另一CNN（称为融合CNN）融合像素级和区域级显着性以形成nal显着图，并且联合学习像素级CNN和融合CNN。对四个公共基准数据集的大量定量和定性实验表明，所提出的方法大大优于最先进的显着性检测方法。-A new saliency detection method by significant regional level estimates and forecast significant pixel level and CNN (expressed as CRPSD) combined. For pixel-level significant prediction to perform a full convolution neural network by modifying VGGNet architecture (called pixel-level CNN) learning to perform multi-scale features, image to image prediction to complete the pixel level detection based on the significant learning . For regional levels significantly estimate, the first generation technology to design image is divided into regions based on adaptive super-pixel area, based on the model of the region through the use of CNN (CNN called regional level) to estimate regional levels significantly. By using another CNN (CNN called fusion) Fusion pixel level and regional level to form nal significant saliency map, and the Joint Learning pixel level fusion CNN and CNN. Four common reference data set of a large number of quantitative and qualitative experiments show that the proposed m Platform: |
Size: 4427776 |
Author:祖祖- |
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Search - AREA BASED IMAGE FUSION