Sobel--of-MATLAB

说明：  基于CV模型的自动分割,能够对灰度图分割操作(image segmentation Level set Chan-Vase)

很全的图像融合指标，综合熵、空间频率、互信息等等，一共7种！和两幅源图像！每个程序都测试过，效果很好，直接出结果(Are all indicators of image fusion, integrated entropy, spatial frequency, mutual information, etc., a total of 7! And the two source image! Each program is tested with good results, directly results from)

利用径向基函数的方法进行隐式表面重建，matlab代码(Radial basis function method for implicit surface reconstruction)

对于空间两个点云，利用经典的icp算法进行匹配(ICP fit points in data to the points in model. Fit with respect to minimize the sum of square errors with the closest model points and data points. )

说明：  机载正侧视SAR雷达仿真和成像，使用matlab编写，直接运行可用(Simulation and imaging of airborne forward-looking SAR radar)

说明：  matlab软件进行近红外光谱图像处理mwpls方法，进行最佳光谱区间的选择(Matlab software for near infrared spectral image processing mwpls method, the best spectral range selection)

说明：  以稀疏基有离散余弦变换基（DCT）和快速傅立叶变换基（FFT）做为稀疏基，高斯随机矩阵、部分哈达玛矩阵为测量矩阵，L1范数、正交匹配追踪算法（OMP）为重建算法进行压缩感知算法实现。 以f = cos(2*pi/256*t) + sin(2*pi/128*t)做为原信号，取原信号f的20%做为输入进行压缩感知重建。(The sparse basis includes discrete cosine transform (DCT) and fast Fourier transform (FFT) as sparse basis, Gaussian random matrix and partial Hadamard matrix as measurement matrix, L1 norm and orthogonal matching pursuit algorithm (OMP) as reconstruction algorithm. In this paper, the reconstructed signal is reconstructed by using the original sensing signal (sinf * t) as the original input signal (i.e. sinf * t * 2) + 2 * s * t * as the input signal.)

说明：  图像配准，利用互信息进行图像配准，主要针对旋转和平移(Image registration using mutual information for image registration, the main response to rotation and translation)

何开明引导滤波去雾算法matlab实现引，导滤波在滤波的同时，具有保边缘的特效，考虑了空间因素，可以用在图像去雾算法中对透射率t的优化中，能起到很好的效果(Matlab implementation of He Kaiming guided filtering algorithm)

说明：  三种emd去噪方法：1、直接使用小波阈值，进行硬阈值去噪；2、使用具有emd分解特性的阈值去噪；3、emd分解后的平移不变去噪。(Three kinds of EMD denoising methods: 1. Direct use of wavelet threshold for hard threshold denoising; 2. Use of threshold denoising with EMD decomposition characteristics; 3. Translation invariant denoising after EMD decomposition.)