BMP

合成孔径雷达（SAR）的线频调变标（CS）成像算法(SAR radar imaging algorithm CS)

将某一8位的raw格式图转换为16位的raw格式图 并可输出raw格式图的灰度值至相应文件（.txt文件） 生成的新图用PS打开时与原图基本一致.(It can converted an 8-bit raw format, to an 16-bit raw format and can output raw format diagram of the gray value to the corresponding map file (. Txt files) .The new plan to open with PS are basically consistent with the Original.)

输入一个带权无向图的顶点数、边数、各条边信息（两个顶点和权值），建立该图的邻接矩阵结构，输出该邻接矩阵。将上述无向图邻接矩阵转换为邻接表结构，输出该邻接表；根据该邻接表对无向图进行深度优先遍历序列和广度优先遍历序列，并输出遍历结果；用prim算法实现构造该带权无向图的最小生成树，并将该最小生成树的各条边信息输出。(Enter a weighted undirected graph with vertices, edges, each of the side information (two vertices and weights), the establishment of the adjacency matrix structure, the output of the adjacency matrix. The above undirected graph adjacency matrix into adjacency list structure, the output of the adjacency list according to the adjacency table undirected graph depth-first traversal sequence and breadth-first traversal sequence, and outputs through the results using prim algorithm construct the weighted undirected graph minimum spanning tree, and the minimum spanning tree information on respective sides of the output.)

insar图像数据，envisat卫星单视复数数据，可在matlab中直接打开(insar data, envisat single look complex data, which can be opened directly in matlab)

说明：  基于MATLAB实现传统图像去噪算法（均值滤波、中值滤波、非局部均值滤波NLM、三维块匹配滤波BM3D）(The traditional image denoising algorithms (mean filter, median filter, NLM, 3D block matching filter BM3D) and dncnn image denoising algorithm based on deep convolution neural network are realized based on MATLAB.)

使用K均值做聚类分析来进行图像分割,把原始图像转换到HSV空间做聚类分析(use k-means clustering to segment the image)

各种边缘检测程序，包括2Sobel算子边缘检测， Prewitt算子边缘检测，Roberts算子边缘检测， Laplace算子边缘检测，Canny算子边缘检测 (Edge Color)

区域生长法的图像分割，可以较好地提取脑部图像。(The region growing method can extract brain image well.)

Aptina ASX340图像传感器设计线路图及datasheet(Aptina ASX340 image sensor design schematics and datasheet)

本书是计算机视觉编程的权威实践指南,依赖 Python 语言讲解了基础理论与算法,并通过 大量示例细致分析了对象识别、基于内容的图像搜索、光学字符识别、光流法、跟踪、三维重建、 立体成像、增强现实、姿态估计、全景创建、图像分割、降噪、图像分组等技术。(This book is an authoritative guide to computer vision programming, which explains basic theories and algorithms based on the Python language.A large number of examples carefully analyze object recognition, content based image search, optical character recognition, optical flow, tracking, 3D reconstruction,Stereoscopic imaging, augmented reality, attitude estimation, panoramic creation, image segmentation, noise reduction, image grouping and other techniques.)