matlab编程技术将灰度图像归一

使用admm算法解决lasso_lsqr问题，matlab代码( U4F7F u7528admm u7B97 u6CD5 u89E3 u51B3lasso_lsqr u95EE u9898 uFF0Cmatlab u4EE3 u7801)

图像压缩感知,稀疏基为FFT或DWT（其中FFT是构造正交变换矩阵，DWT是对高频系数进行测量）；重构方法为OMP(compression sensing of image based on FFT or DWT)

说明：  可以通过光流法来检测运动的目标，具有很好的效果，适合新手了解(It can detect moving target by optical flow method, and can detect moving target by optical flow method, which has good effect and is suitable for novice to understand)

首先生成如下形式的图片：图中的白条为7象素宽，210象素高，白条间的宽度为17象素。 应用多种滤波器进行处理：算术均值，几何均值，谐波均值，逆谐波均值，中值滤波，中点滤波，最大值、最小值滤波等。注意，需改变模板大小。详细比较上述的结果，并分析原因。 (First generation following form images: figure IOUs 7 pixels wide, 210 pixels high, the width is 17 pixels between IOUs. Using a variety of filters for processing: the arithmetic mean, geometric mean, harmonic mean, inverse harmonic mean, median filtering, filtering midpoint, maximum, minimum filtering. Note that the need to change the template size. A detailed comparison of these results and analyze the reasons.)

读取gpr数据，并且对数据进行成像，没有进行任何数据处理(Read the GPR data and image the data without any data processing)

基于活动轮廓模型的图像分割算法，具体算法细节，可以见压缩包中的论文(Based on Active Contour Model of image segmentation algorithm, the details of the specific algorithm, you can see the archive of papers)

图割（GC)和主动轮廓模型（ACM)相结合的一种图像分割方法，既可以进行局部图像的分割，也可进行全局图像的分割，简单实用。(A combination of graph cuts (GC) and the active contour model (ACM) image segmentation method, either local image segmentation, global image segmentation, simple and practical.)

本算法将进行人脸识别，首先进行人脸定位检测，然后根据数据库里给的标签进行人脸识别，将人脸的信息显示在人脸旁边。本算法能取得较好的效果。(The algorithm will perform face recognition, first face detection, and then according to the database to the label for face recognition, the face of the information displayed next to the face. The algorithm can achieve better results.)

基于小波阈值法来实现图像去噪，这是小波阈值去噪的程序代码。(Based on wavelet thresholding denoising method to achieve this is wavelet thresholding procedure code.)

利用彩色蛇模型对彩色图像进行处理，即不将彩色图像分为Ｒ，Ｇ，Ｂ三个分量，而是将彩色图像视为单一簇的处理，反而会得到更好的边缘清晰度(The use of color snake model for color image processing, that is, not the color image into R, G, B three-component, but rather as a single cluster of color image processing, it will get better edge sharpness)