Description: 稀疏分解图像重建程序，把图像分解成多个小块图像，然后再各个子块重建后边缘处理后合并成整个图像。-sparse decomposition image reconstruction process, the image is divided into a number of small images, then each sub-block redevelopment edge after the merger into the whole image. Platform: |
Size: 63488 |
Author:fanghui20006 |
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Description: （1）应用9×9的窗口对上述图象进行随机抽样，共抽样200块子图象；
（2）将所有子图象按列相接变成一个81维的行向量；
（3）对所有200个行向量进行KL变换，求出其对应的协方差矩阵的特征向量和特征值，按降序排列特征值以及所对应的特征向量；
（4）选择前40个最大特征值所对应的特征向量作为主元，将原图象块向这40个特征向量上投影，所获得的投影系数就是这个子块的特征向量。
（5）求出所有子块的特征向量。
-(1) the application of 9 × 9 window of these images at random, a total sample of 200 sub-image (2) all sub-images according to out-phase into a 81-dimensional row vector (3) all 200 lines for KL transform vector, derived its corresponding covariance matrix of eigenvectors and eigenvalues, in descending order by eigenvalue and the corresponding eigenvector (4) a choice to 40 corresponding to the largest eigenvalue eigenvector as the PCA, the original image block to the 40 feature vectors on the projection, the projection coefficients obtained by this sub-block eigenvector. (5) calculated for all sub-block eigenvector. Platform: |
Size: 64512 |
Author:ly |
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Description: 利用Sub-pattern PCA在Yale人脸库上进行人脸识别的matlab源代码，子模式主成分分析首先对原始图像分块，然后对相同位置的子图像分别建立子图像集，在每一个子图像集内使用PCA方法提取特征，建立子空间。对待识别图像，经相同分块后，分别将子图像向对应的子空间投影，提取特征。最后根据最近邻原则进行分类。-Sub-pattern PCA use in the Yale face database for face recognition on the matlab source code, sub-mode principal component analysis first of the original image block, and then the same sub-image, respectively, the location of the establishment of sub-image set, in each sub-image Set the use of PCA to extract the features, the establishment of sub-space. Treatment to identify images, by the same block, the respective sub-image to the corresponding sub-space projection, feature extraction. Finally, according to the principle of nearest neighbor classification. Platform: |
Size: 2048 |
Author:章格 |
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Description: 彩色图像分形压缩，由R,G，B中确定一个R块与D块的匹配位置，其他二原色只需在同一个D块位置匹配即可，只改变对应的亮度和偏移量即可，比SFC的方法节省时间。-Fractal color image compression, by the R, G, B defined in a R Block and D Block matching location, the other two primary colors just in the same location matching block D can only change the corresponding brightness and can offset than SFC method to save time. Platform: |
Size: 611328 |
Author:鞠金玲 |
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Description: 子模式主成分分析首先对原始图像分块，然后对相同位置的子图像分别建立子图像集，在每一个子图像集内使用PCA方法提取特征，建立子空间。对待识别图像，经相同分块后，分别将子图像向对应的子空间投影，提取特征。最后根据最近邻原则进行分类。-Sub-mode principal component analysis first of the original image block, and then the same sub-image, respectively, the location of the establishment of sub-image set, in each sub-image set to use PCA to extract the features, the establishment of sub-space. Treatment to identify images, by the same block, the respective sub-image to the corresponding sub-space projection, feature extraction. Finally, according to the principle of nearest neighbor classification. Platform: |
Size: 165888 |
Author:tanghui |
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Description: （1）应用9×9的窗口对上述图象进行随机抽样，共抽样200块子图象；
（2）将所有子图象按列相接变成一个81维的行向量；
（3）对所有200个行向量进行KL变换，求出其对应的协方差矩阵的特征向量和特征值，按降序排列特征值以及所对应的特征向量；
（4）选择前40个最大特征值所对应的特征向量作为主元，将原图象块向这40个特征向量上投影，所获得的投影系数就是这个子块的特征向量。
（5）求出所有子块的特征向量。
-(1) the application of 9 × 9 window of these images at random, a total sample of 200 sub-image (2) all sub-image by out-phase into a 81-dimensional vector lines (3) All 200 line vector KL transform, derive its corresponding covariance matrix eigenvectors and eigenvalues, in descending order eigenvalues and corresponding eigenvectors (4) a choice to 40 corresponding to the largest eigenvalue eigenvector as the main element, the original image block to the 40 on the projection eigenvector obtained projection coefficient is the sub-block eigenvector. (5) calculated for all sub-block eigenvector. Platform: |
Size: 2048 |
Author:龙活 |
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Description: 针对传统方法难以可靠估计图像中纹理单一区域像素点视差的问题,将纹理分析应用于立体匹配中,提
出图像分块整体匹配的方法。首先用LBP/C纹理分析方法对图像纹理进行描述 然后进行基于区域生长的扩张检
测,得到纹理单一图像块 最后对图像块进行整体匹配,得到纹理单一区域的稠密视差图。对国际标准图像进行测试,
结果表明该算法能提高纹理单一区域稠密视差图的精度,具有实用价值
-Due to the difficulty of getting disparity of less textured pixels with traditional approach, block matching
algorithm with texture analysis was put forward. First, the images were described by LBP/C texture analysis. Then the
expansion detection based on region growing was performed, and the less textured blocks were acquired. Finally the dense
disparitymap of less textured area was obtained through block matching. The proposed algorithm was tested with the
international standard image data and comparedwith some existing algorithms. The experimental results show that the proposed
algorithm can improve the accuracy of disparitywhen handling less textured areas and can be used in practic
Platform: |
Size: 257024 |
Author:swx |
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Description: 已知两个不同图像块亮度数据如下：
（1）分析DCT原理，采用DCT方法，编程并计算相应的DCT系数，分析系数分布特点。
（2）依据视觉特性分析量化表步长的分布特点，完成DCT系数量化。
（3）采用Z形扫描，实现输出数据的统计编码，形成Video stream。
（4）采用IDCT重建图像亮度数据，计算SAD大小，分析产生误差的原因及采用DCT进行数据压缩的原理。（ ）
（5）分别利用左上角1、3、6个系数重建图像，计算相应的SAD，并由此分析直流和低频系数的重要性。
-Known brightness of two different image data block is as follows: (1) Principles of analysis of DCT, the DCT method, program and calculate the corresponding DCT coefficient, analysis of distribution coefficients. (2) quantitative analysis based on visual characteristics of the distribution of long-form features step-by-step to complete the DCT coefficient quantization. (3) the use of Z-scan output data to achieve statistical encoding, the formation of Video stream. (4) the use of the reconstructed image brightness IDCT data to calculate the SAD size, analysis of the causes of errors and the use of DCT for the principle of data compression. () (5), respectively, a factor of 1,3,6 using the upper left corner of the reconstruction of images, calculating the corresponding SAD, and the resulting analysis of DC and the importance of low-frequency coefficients. Platform: |
Size: 1024 |
Author:张元 |
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Description: Block Matching Algorithms for Motion Estimation
This project contains the project report and source code by Aroh Barjatya for Digital Image Processing Class at Utah State University.
Following is a short description of the m files in this zip
motionsEstAnalysis.m Script to execute all Algorithms
motionEstES.m Exhaustive Search Algorithm
motionEstTSS.m Three Step Search Algorithm
motionEstNTSS.m New Three Step Search Algorithm
motionEstSESTSS.m Simple And Efficient Search Algorithm
motionEst4SS.m Four Step Search Algorithm
motionEstDS.m Diamond Search Algorithm
motionEstARPSm Adaptive Root Pattern Search Algorithm
costFuncMAD.m Mean Absolute Difference Function
minCost.m minimum cost among macro blocks
motionComp.m motion compensated image creator
imgPSNR.m finds image PSNR w.r.t. reference image
The test images can be found at http://cc.usu.edu/~arohb/caltrain.zip-Block Matching Algorithms for Motion Estimation
This project contains the project report and source code by Aroh Barjatya for Digital Image Processing Class at Utah State University.
Following is a short description of the m files in this zip
motionsEstAnalysis.m Script to execute all Algorithms
motionEstES.m Exhaustive Search Algorithm
motionEstTSS.m Three Step Search Algorithm
motionEstNTSS.m New Three Step Search Algorithm
motionEstSESTSS.m Simple And Efficient Search Algorithm
motionEst4SS.m Four Step Search Algorithm
motionEstDS.m Diamond Search Algorithm
motionEstARPSm Adaptive Root Pattern Search Algorithm
costFuncMAD.m Mean Absolute Difference Function
minCost.m minimum cost among macro blocks
motionComp.m motion compensated image creator
imgPSNR.m finds image PSNR w.r.t. reference image
The test images can be found at http://cc.usu.edu/~arohb/caltrain.zip Platform: |
Size: 118784 |
Author:Yashil |
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Description: 提出了一种新的基于离散小波变换(DWT)与奇异值分解(SVD)相结合的数字图像水印算法。该算法将原始图像作小波分解并将小波分解得到的低频子带进行分块，对每一块进行奇异值分解后，选取每块中最大的奇异值通过量化的方法嵌入经过Arnold置乱后水印信息。-A new wavelet transform based on discrete (DWT) and Singular Value Decomposition (SVD) combination of digital image watermarking algorithm. The algorithm for wavelet decomposition of the original image and wavelet decomposition low-frequency sub-band are divided into blocks, each a singular value decomposition, the selection of each block by the largest singular value methods to quantify Arnold scrambled after embedding the watermark . Platform: |
Size: 38912 |
Author:久久 |
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Description: 此matlab代码用于削减并消除图片中的block，提高图片质量。Deblock使用小波变换消除block，DeBlocking2通过计算熵，确定每个block的信息量，来消除block-This matlab code is for diluting and removing artifacts in images, upgrading the images. DeBlock applies wavelet to deblock and DeBlocking2 deblocks by calculating entropy, classifying amount of information, in each block. Platform: |
Size: 7168 |
Author:Yuou Jiang |
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Description: 手持式摄像机在使用时常常会受到使用者有意无意抖动的影响,从而影响成像效果，造成录制视频的不稳定及跳动问题，尤其是在使用者在一场景中特写或者跟踪某一具体目标时，使用者通常不能准确定位到或者估计出运动目标的位置，从而造成目标在视频中位置的不稳定，造成视频的主观效果变得不理想。
为了解决这一问题，我们需要设计一种算法来识别这种无意义的运动并设法通过补偿的方式来使得场景中的目标物体保持位置稳定的状态。
手持式摄像机捕获的视频通常都会受到抖动的影响，这严重的影响视频的主观效果。
这里提出的算法可以可靠的用于数字视频的去抖动。这个算法通过识别这些意外的抖动并且利用运动补偿的方法来获得一个较好的视频输出。
这个系统可以分为三个模块：（1）运动估计模块；（2）抖动识别模块；（3）运动补偿模块。
注：代码在matlab 7.7.0(R2008b)运行通过
[1] F Vella, A Castorina, M Mancuso. Digital image stabilization by adaptive block motion vectors filtering, IEEE Transactions on Consumer Electronics, 2002-Handheld cameras are often used by the user intentionally or unintentionally, the influence of jitter affect the imaging results, resulting in instability and beat the problem of recording video, and especially in the user in a scene close-up or tracking a specific target,users often can not accurately navigate to estimate the location of the moving target, resulting in the subjective effects of target location of the instability in the video, resulting in video not ideal.
In order to solve this problem, we need to design an algorithm to identify such a meaningless exercise and try to make the object in the scene to keep the position stable state by way of compensation.
The handheld camera captured video usually subject to the influence of jitter, the subjective effect of which seriously affect the video.
The algorithm proposed here can be reliably used for digital video to jitter. This algorithm by identifying the jitter of these accidents and the use of motion compensation m Platform: |
Size: 2348032 |
Author:Andy He |
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Description: 指纹增强的matlab实现源代码，包含多个matlab函数文件。-ridgesegment.m identifies ridge-like regions of a fingerprint image. It also normalises the intensity values of the image.
ridgeorient.m estimates the local orientation of ridges in a fingerprint.
plotridgeorient.m plots ridge orientations calculated by ridgeorient.
ridgefreq.m estimates the local ridge frequency across a fingerprint image.
freqest.m estimates the ridge frequency within a small block of an image. This is used by ridgefreq.
ridgefilter.m enhances a fingerprint image using oriented filters.
Platform: |
Size: 10240 |
Author:tc |
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Description: 编程实现二维8*8图像块的DFT，DCT变换
输入一副RGB图像，将其转化为YCbCr颜色空间，然后对Y分量做分块DFT变换，保存为灰度图像，统计计算时间
编程实现8*8块DCT变换,然后分别显示，当使用64个DCT系数里面的一些系数进行重构，比较其质量；同时熟悉采用FFT来实现DCT变换的思想，并显示2维8*8DCT变换的基函数图像(Programming the DFT, DCT transform of two dimensional 8*8 image block
Input a pair of RGB images, transform it into YCbCr color space, then divide the Y component into block DFT transform, save as gray image, and calculate the calculation time
The 8*8 block DCT transform is implemented by programming, and then it is shown that when using some coefficients in 64 DCT coefficients, the quality is reconstructed. Meanwhile, the idea of DCT transformation is familiar with FFT, and the base function image of the 2 dimensional 8*8DCT transform is displayed.) Platform: |
Size: 3072 |
Author:雪鹊
|
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Search - block by block image in matlab