deconv_sondhi.m

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.)

说明：  K均值作为图像分割的经典算法，新手必学算法。利用K均值进行图像分割，关于分类个数可以进行人为设定。(As a classical algorithm for image segmentation, k-means is a must-learn algorithm for beginners. The k-means is used for image segmentation, and the number of classification can be set artificially.)

图像分割，完整程序，验证可行。新手上传，望支持。(image direct)

Ncut的文献及matlab源代码。基于图的分割非常好的一个算法(Ncut literature and matlab source code. Graph-Based Segmentation Algorithm for a very good)

进行纯净信号和加噪信号的时频域显示，及其包络图显示，并计算加噪信号的信噪比、互相关系数、峭度值，对信号进行离散余弦变换。(The time-frequency domain display of pure signal and noise-added signal and its envelope diagram display are carried out, and the signal-to-noise ratio, correlation coefficient and kurtosis value of noise-added signal are calculated.Making Discrete CoSine transform for signal)

这是一本关于超分辨的英文书籍，希望对大家有用1(This is a english book about super-resolution,hope to help you!)

matlab编写Zernike多项式拟合横向剪切波前重建。(matlab write Zernike polynomial fitting transverse shear wave before reconstruction.)

可以实现图像的重采样，通过设置参数来减小或增大图像尺寸(Resampling of the image can be achieved by setting parameters to decrease or increase the image size)

个人收集的一些关于小波理论在matlab中实现的文章(Personal collection of some articles on wavelet theory in matlab)

基于纹理和边缘方向特征融合的目标跟踪程序代码，是基于mean shift框架的(This paper proposes a powerful and robust local descriptor,called textureorientation descriptor(TOD). TOD consists of two components: texture and orientation. Considering that the human shape shows more short, fragmentary and vertical edges, the proposed TOD descriptor encodes the spatial information of pedestrian image. We combine the proposed TOD representation model with mean shift framework to perform pedestrian tracking. Results from experiments show the improved performance of the proposed algorithm, in the scenarios of heavy occlusions, illumination variations and similar objects interference, when compared with the other descriptors based trackers.)