Description: IPL库是Intel为了配合其MMX,SSE,SSE2以及将来的VLSW等技术发布的图像处理库
支持1,8,16,位有/无符号，32位有符号，32位浮点类型数据类型。
支持RGB,CMYK,YCACB,YUV,XYZ,色彩空间，支持alpha通道
支持矩形ROI,通道ROI，遮罩
支持分块图像，错误处理，用户定义函数
支持图像数学，几何，滤波，图象统计，色彩空间变换操作。-IPL is the database to accommodate its Intel MMX, SSE, SSE2 and future technologies such as VLSW issued by the image processing support for 1, 8, 16, spaces with / without symbols, and symbols are 32, 32-bit floating-point type data types. Support for RGB, CMYK, YCACB, YUV, XYZ color space, support alpha channel support rectangular ROI, ROI access, support shielding block images, error handling, user-defined functions to support image mathematics, geometry, filtering, image statistics, color space conversion operations . Platform: |
Size: 163031 |
Author:魏刚祥 |
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Description: Using Guide:
VC++6.0
employ the command in the menu step by step:
image smooth
image normalization
Draw Dot direction
Draw Block direction
Draw Smooth direction
Gabor filters enhancement
\"readme.txt\" file: this is the introduce file for the algorithm
http://yangjucheng.chonbuk.ac.kr/
--------------------------------------------------
Dr. JuCheng Yang,
Division of Electronics & Information Engineering,
Chonbuk National University,
664-14 1Ga Deonjin-dong Deonjin-gu,
Jeonju, Jeonbuk, 561-756, Korea.
Email: biometricscode@gmail.com Platform: |
Size: 162165 |
Author:洋洋 |
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Description: 金字塔算法。将图像数组分块保存为4维数组，显示的时候仅仅调用窗口大小的部分，缩短了显示所需要的时间-pyramid algorithm. The image array block for the preservation of four-dimensional arrays, showing only the window size of the call, shorten the show by the time Platform: |
Size: 12495872 |
Author:李剑 |
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Description: IPL库是Intel为了配合其MMX,SSE,SSE2以及将来的VLSW等技术发布的图像处理库
支持1,8,16,位有/无符号，32位有符号，32位浮点类型数据类型。
支持RGB,CMYK,YCACB,YUV,XYZ,色彩空间，支持alpha通道
支持矩形ROI,通道ROI，遮罩
支持分块图像，错误处理，用户定义函数
支持图像数学，几何，滤波，图象统计，色彩空间变换操作。-IPL is the database to accommodate its Intel MMX, SSE, SSE2 and future technologies such as VLSW issued by the image processing support for 1, 8, 16, spaces with/without symbols, and symbols are 32, 32-bit floating-point type data types. Support for RGB, CMYK, YCACB, YUV, XYZ color space, support alpha channel support rectangular ROI, ROI access, support shielding block images, error handling, user-defined functions to support image mathematics, geometry, filtering, image statistics, color space conversion operations . Platform: |
Size: 162816 |
Author:魏刚祥 |
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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: 提出一种基于视觉特性的图像摘要算法，增大人眼敏感的频域系数在计算图像Hash时的权重，使得图像Hash更好地体现视觉特征，并提高鲁棒性。将原始图像的分块DCT系数乘以若干由密钥控制生成的伪随机矩阵，再对计算的结果进行基于分块的Watson人眼视觉特性处理，最后进行量化判决产生固定长度的图像Hash序列。本算法比未采用视觉特性的算法相比，提高了对JPEG压缩和高斯滤波的鲁棒性。图像摘要序列由密钥控制生成，具有安全性。-Based on the visual characteristics of the image digest algorithm, increasing the human eye-sensitive frequency-domain coefficients in the calculation of the image when the weight of Hash, Hash makes images better reflect the visual characteristics, and improve robustness. Will block the original image multiplied by the number of DCT coefficients generated by the key control of pseudo-random matrix, then the results of calculation based on the sub-block of Watson HVS treatment, and finally quantify the judgments arising from fixed-length sequence of images Hash . Than the algorithm did not use the visual characteristics of the algorithm, improve the JPEG compression and Gaussian filtering robustness. Abstract image sequence generated by the key control, with security. Platform: |
Size: 167936 |
Author:kurt |
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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: 一種基于塊樹結構的SPIHT數字圖像壓縮算法: 在分析圖像經過小波變換后所具有的特性的基礎上,提出了一種改進的SPIHT算法.由于低頻系數占據圖像的百分之九十以上的能量,在圖像的重構中十分重要,所以,對這些系數不進行壓縮而直接傳輸 而最高頻系數相對不重要,所以不做處理,只在圖像重構時以指定的數值予以重構,因此不僅提高了圖像的質量,同時提高了圖像的壓縮率.在具體的算法中,提出了塊樹的結構,減少了算法所需的內存,拓寬了算法的應用范圍.-A tree structure based on the SPIHT block of digital image compression algorithm: After the analysis of images after the wavelet transform features, based on a modified SPIHT algorithm. Due to the low frequency coefficient images occupy more than 90 percent of energy in image reconstruction is very important, so these factors do not directly transfer compression The most high-frequency coefficient is relatively unimportant, so no treatment, only when the image reconstruction be designated numerical reconstruction therefore not only improve the image quality, while improving the image compression ratio. algorithms in specific, the proposed block-tree structure, the algorithm to reduce memory requirements and broaden the scope of application of the algorithm. Platform: |
Size: 273408 |
Author:jason.. |
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Description: 本程序主要四大功能特点:
1、继承了 YUVViewer 可以同时打开多窗口的特点；
2、继承了 Elecard YUV Viewer 可以进行两幅图像对比的特点；
3、增加了宏块信息显示功能；
4、增加了两图对比时同步帧跳转功能：
例如对于错误码流，不同的错误隐藏算法可能造成解码序列长度不同，这时要比较两个序列就很不方便。而该功能正是为了解决这个问题，即以另一序列的显示图像为标准，在当前序列中寻找与其完全相同的图像。 -Four main features of this procedure: 1, inherited YUVViewer can also open multiple windows of the characteristics 2, inherited Elecard YUV Viewer can be characterized by comparing two images 3, an increase of the macro block information display 4, increasing When comparing the two plans simultaneously frame jump function: for example, the error stream for the different error concealment algorithm for decoding the sequence length may result in different time sequences to compare two very inconvenient. And the function is precisely to solve this problem, that is another sequence of display images as the standard sequence search in the current image with exactly the same. Platform: |
Size: 230400 |
Author:沈磊 |
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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: 用于从图像中提取不同大小的块，采样可选为重叠，不重叠和随机，以及由块恢复图像的三个函数-Used to extract from the image blocks of different sizes, optional sampling overlapping, non-overlapping and random, and by block to restore the image of the three functions Platform: |
Size: 2048 |
Author:xuhongteng |
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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: 对一幅给定图像进行分块，且能记录每一分块，并分别对每块进行操作。同时附有实验图像。-For a given image block, and can record each block, and each operation separately. Accompanied by experimental image. Platform: |
Size: 214016 |
Author:张娜 |
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Description: 寻找图像中的连通区域，逐块寻找得到连同区域并用不同灰度标记。-Find the connected region, block by block to find together with the area and marked with different gray-scale image Platform: |
Size: 1024 |
Author:sinasina |
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Description: DCT and Image Compression
In the JPEG image compression algorithm, the input image is divided into
8-by-8 or 16-by-16 blocks, and the two-dimensional DCT is computed for each
block. The DCT coefficients are then quantized, coded, and transmitted. The
JPEG receiver (or JPEG file reader) decodes the quantized DCT coefficients,
computes the inverse two-dimensional DCT of each block, and then puts the
blocks back together into a single image. For typical images, many of the
DCT coefficients have values close to zero these coefficients can be discarded
without seriously affecting the quality of the reconstructed image.
The example code below computes the two-dimensional DCT of 8-by-8 blocks
in the input image, discards (sets to zero) all but 10 of the 64 DCT coefficients
in each block, and then reconstructs the image using the two-dimensional
inverse DCT of each block. The transform matrix computation method is used.- DCT and Image Compression
In the JPEG image compression algorithm, the input image is divided into
8-by-8 or 16-by-16 blocks, and the two-dimensional DCT is computed for each
block. The DCT coefficients are then quantized, coded, and transmitted. The
JPEG receiver (or JPEG file reader) decodes the quantized DCT coefficients,
computes the inverse two-dimensional DCT of each block, and then puts the
blocks back together into a single image. For typical images, many of the
DCT coefficients have values close to zero these coefficients can be discarded
without seriously affecting the quality of the reconstructed image.
The example code below computes the two-dimensional DCT of 8-by-8 blocks
in the input image, discards (sets to zero) all but 10 of the 64 DCT coefficients
in each block, and then reconstructs the image using the two-dimensional
inverse DCT of each block. The transform matrix computation method is used. Platform: |
Size: 1024 |
Author:Eldhose |
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Description: 一种适用于图像的压缩感知采样策略和重建算法，采样策略是基于块的图像稀疏采样矩阵，重建算法为smoothed projected Landweber（SPL）迭代算法。-BCS-SPL combines block-based compressed-sensing sampling (BCS) of an image with a smoothed projected-Landweber (SPL) iterative reconstruction. Sampling is driven by random matrices applied on a block-by-block basis, while the reconstruction is a variant of projected Landweber (PL) reconstruction (also known as iterative hard thresholding (IHT)) that incorporates a smoothing operation (Wiener filtering) intended to reducing blocking artifacts. In essence, this filtering operation imposes smoothness in addition to the sparsity inherent to PL. Platform: |
Size: 1163264 |
Author:孔德地 |
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Description: BCS-SPL将图像的基于块的压缩感测采样（BCS）与平滑的投影Landweber（SPL）迭代重建相结合。采样是通过逐块应用随机矩阵来驱动的，而重建则是预期的Landweber（PL）重建（也称为迭代硬阈值（IHT））的变体，其结合平滑操作（维纳滤波）减少块效应。实质上，除了PL所固有的稀疏性之外，这种滤波操作还能提供平滑性。(BCS-SPL combines block-based compressed-sensing sampling (BCS) of an image with a smoothed projected-Landweber (SPL) iterative reconstruction. Sampling is driven by random matrices applied on a block-by-block basis, while the reconstruction is a variant of projected Landweber (PL) reconstruction (also known as iterative hard thresholding (IHT)) that incorporates a smoothing operation (Wiener filtering) intended to reducing blocking artifacts. In essence, this filtering operation imposes smoothness in addition to the sparsity inherent to PL.) Platform: |
Size: 1162240 |
Author:钟易xxxx |
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Description: (1) 读取图片 ，转换为灰度图像；
(2) 对 view1.png view1.png view1.png view1.png和 View5 .png .png 将图像 按照 4x4 像素 /方格 的形式 进行 分块；
(3) 考虑 边缘的相对稳定 性，以及 双目 成像 视差 规律 ，在第一幅图像分割得 到的块图像周围 20 个像素 个像素 的距离区间内由近到远进行搜索，寻找 与该块 欧氏距离最近的块作为新位置 ；
(4) 计算 视差 ，将每个小块 中代表点 的视差 信息转换到整个 区间 ；
(5) 将视差转 换为 深度，并归一化到 0-255 区间内 ，并显示图片 ；((1) read the picture and convert it to gray image.
(2) view1.png, view1.png view1.png view1.png and View5.Png.Png are partitioned according to 4x4 pixel / grid.
(3) considering the relative stability of the edge and the parallax rule of the binocular imaging, the nearest to the nearest block of the block's Euclidean distance is searched as a new position in the distance between the 20 pixels of the block image divided by the first image.
(4) calculate parallax and transform the parallax information of the representative points in each block to the whole interval.
(5) convert parallax to depth and normalize to 0-255 interval and display pictures.) Platform: |
Size: 8218624 |
Author:yangxm2011 |
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Search - block by block image