资源列表
BM4D_v2p1
- matlab BM4D代码，可以使用demo去噪(matlab BM4D please use demo to denoise)
3D脑部MRI分割
- 图像分割mr灰白质，脑腔，好几个文件，要在matlab下都运行，先运行serve那个。挺好用的(Image segmentation Mr Gray matter, brain cavity, several files, to run in matlab, run that first. Good use of)
Speckle_reduction
- 用于SAR图像的相干斑抑制，内有增强Lee滤波，增强Kuan滤波，增强GammaMap滤波算法，还有等效视数，边缘保持指数的求法(For speckle suppression of SAR images, there are enhanced Lee filtering, enhanced Kuan filtering, enhanced GammaMap filtering algorithm, and the calculation of ENL and EPI.)
HOG+SVM进行图片中行人检测
- 行人检测HOG+SVM进行图片中行人检测，提供训练用的pos和neg样本，效果还可以；没有SVM工具箱的，压缩包里已经提供了，安装一下即可(Pedestrian detection HOG + SVM for pedestrian detection in pictures, providing POS and neg samples for training, the effect is good; without SVM toolbox, the compression package ha
CEM
- cem检测算法，基于matlab，用来做高光谱目标检测(CEM detection algorithm, based on matlab, for hyperspectral target detection)
surf
- 图像特征提取算法--SURF算法Matlab代码(SURF algorithm matlab code)
CD_Programs
- 合成孔径雷达成像：算法与实现一书中所附数据的提取代码，已经经过提取为CDdata1.mat和CDdata2.mat文件，可直接进行后续仿真。(Synthetic Aperture Radar Imaging: Algorithms and Implementation of the book's data extraction code, has been extracted into CDdata1. mat and CDdata2. mat files, can be directly fo
SAR合成孔径雷达图像点目标(附matlab代码)
- SAR合成孔径雷达的点目标仿真报告，含matlab代码(SAR synthetic aperture radar point target simulation report, including Matlab code)
Colmap
- COLMAP开源源码 COLMAP是一种通用的动态结构（SfM）和多视图立体声（MVS）管道，具有图形和命令行界面。可以方便的对一系列二维图片进行三维重建不用对摄像机进行标定，只需要从不同角度对重建场景或物体进行拍摄得到一系列图像作为输入。(Colmap open source COLMAP is a general-purpose Structure-from-Motion (SfM) and Multi-View Stereo (MVS) pipeline with a graphical
FCRN-DepthPrediction-MATLAB
- 使用matlab搭建神经网络，从单目图像中估计深度值(Using matlab to build a neural network to estimate depth from monocular images)
KDE
- 根据KDE原理，通过训练集训练，能够检测图片中运动的部分，将运动和静止的部分分别用白色和黑色表示。(KDE principle, motion detection, through training set, can detect the moving part of the picture, the moving part and the static part are expressed in white and black respectively.)
Unet-master1
- 适用对象：小样本数据。功能：分割各种类型图像。评价：效果良好的深度学习算法。(Applicable object: small sample data. Function: Segmentation of various types of images. Evaluation: A good deep learning algorithm.)