搜索资源列表
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数字图像矩阵数据的显示及其傅立叶变换
二维离散余弦变换的图像压缩
采用灰度变换的方法增强图像的对比度
直方图均匀化
模拟图像受高斯白噪声和椒盐噪声的影响
采用二维中值滤波函数medfilt2对受椒盐噪声干扰的图像滤波
用MATLAB中的函数filter2对受噪声干扰的图像进行均值滤波
图像的自适应魏纳滤波
运用5种不同的梯度增强法进行图像锐化
图像的高通滤波和掩模处理
用巴特沃斯(Butterwo
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小波软阈值去噪soft harr.rar 将文件所在目录设为工作目录,然后打开wavlet.fig,在noise提示框下输入噪声强度,在0-0.1之间(不能为零)。然后点process按钮,就会显示实验结果,包括原图像,加噪图像,去噪图像的对比以及当前的psnr值。 wavlet.m是程序文件。-wavelet soft thresholding soft harr.rar file directory as the working directory. wavlet.fig then open
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小波软阈值去噪soft harr.rar 小波软阈值去噪soft harr.rar 将文件所在目录设为工作目录,然后打开wavlet.fig,在noise提示框下输入噪声强度,在0-0.1之间(不能为零)。然后点process按钮,就会显示实验结果,包括原图像,加噪图像,去噪图像的对比以及当前的psnr值。 wavlet.m是程序文件。-wavelet soft thresholding wavelet soft harr.rar soft thresholding soft harr.r ar
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anisodiff.m Function to perform anisotropic diffusion of an image following Perona and Malik s algorithm. This process smoothes regions while preserving, and enhancing, the contrast at sharp intensity gradients
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anisodiff.m Function to perform anisotropic diffusion of an image following Perona and Malik s algorithm. This process smoothes regions while preserving, and enhancing, the contrast at sharp intensity gradients.
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Perona和Malik在热传导方程的基础上,建立了各向异性扩散方程并将其应用到了图像处理中尺度空间的图像平滑上,该模型针对图像的边缘部分和非边缘部分进行不同性质的图像处理,同时解算方程的迭代格式简单,克服传统滤波的主要缺陷,是图像质量大为改善,为图像的边缘检测和增强开辟了新的方向。-This process smoothes regions while preserving, and enhancing, the contrast at sharp intensity gradients.
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Perona和Malik在热传导方程的基础上,建立了各向异性扩散方程并将其应用到了图像处理中尺度空间的图像平滑上,该模型针对图像的边缘部分和非边缘部分进行不同性质的图像处理,同时解算方程的迭代格式简单,克服传统滤波的主要缺陷,是图像质量大为改善,为图像的边缘检测和增强开辟了新的方向。-This process smoothes regions while preserving, and enhancing, the contrast at sharp intensity gradients.
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For images with color, it seems obvious that an additional term must be added to consider the difference between the color of the background and the color of the target. In other words, if at present the contrast between the target and the background
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code produce twomodegassian distribution for intensity level which suitable for contrast inhancement
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图像邻域处理,能进行高斯转换,对比度变化,灰度变化,和各种转换-Neighborhood image processing, can be Gaussian conversion, contrast change, intensity change, and a variety of conversion
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基于小波变换的图像去噪的matlab程序
程序使用说明:
1、软件应用平台:Matlab6.5或更高;
2、打开方法:
将文件所在目录设为工作目录,然后打开wavlet.fig,在noise提示框下输入噪声强度,在0-0.1之间(不能为零)。
然后点process按钮,就会显示实验结果,包括原图像,加噪图像,去噪图像的对比以及当前的psnr值。
wavlet.m是程序文件。
程序内容写在在程序的注释里。
阈值的更改没有实现可视化,在源程序中可以改。-Image d
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this contains m files for image mainpulation like contrast stretching,bit-plane slicing,image negative,directional smoothing and intensity increase.It uses user defined function for laplacian edge calculation and convolution.
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AM MODULATION: It is matlab code for AM,DSBSC,SS B with carrier ,ISB, & multitone AM & their spectrum. AM is a technique used in electronic communication, most commonly for transmitting information via a radio carrier wave. AM works by varying the st
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调节显示器亮度: 通过VC程序实现了显示器的亮度对比度调节,通过不同的OPENCV函数调用展示了底层开发的基本技术。-Light Intensity Configuration: Based on VC++, codes realize the adaptive configuration of monitor s light intensity and contrast ratio. By the use of different OPENCV functions, codes show th
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Gamma_Manager_Demo: 实现了利用Gamma改变屏幕亮度值以及对比度的程序。通过不同的OPENCV函数调用展示了底层开发的基本技术。-Gamma_Manager_Demo: Codes realize the control of monitor light intensity and contrast ratio based on Gamma. By the use of different OPENCV functions, codes show the basic tech
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同态滤波去噪,把频率过滤和灰度变换结合起来的一种图像处理方法,它依靠图像的照度/ 反射率模型作为频域处理的基础,利用压缩亮度范围和增强对比度来改善图像的质量。使用这种方法可以使图像处理符合人眼对于亮度响应的非线性特性,避免了直接对图像进行傅立叶变换处理的失真。-Homomorphic filtering noise, the frequency filter and the gradation conversion processing method combining an image, wh
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医学图像配准,医学图像2D_3D配准,基于vc++开发。-Abstract. We present a method that performs the rigid 2D/3D image registration
efficiently on the GPU. As one main contribution of this paper, we propose an
efficient method for generating realistic DRRs that are visu
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by traditional level set based segmentation models. In this paper, we propose a new level set
method integrating local and global intensity information adaptively to segment inhomogeneous images. The
local image information is associated with the
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Image segmentation is among the most studied problems in image understanding and
computer vision. The goal of image segmentation is to partition the image plane into
a set of meaningful regions. Here meaningful typically refers to a semantic part
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liver contrast-enhanced CT images
that uses shape-intensity prior level set combining probabilistic
atlas and probability map constrains. We first weight all of
the atlases in the selected training datasets by calculating the
similarities bet
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