资源列表
qianjingfenge(matlab)
- 基于matlab的前景背景分割，对数字图像处理感兴趣的可以看看啊-Background segmentation based matlab prospect of digital image processing are interested can look ah
Overlay-Pattern-Matching-
- 通过labview中的图像处理技术，来处理多模板的匹配技术-Through the image processing technology of labview, to deal with multiple template matching technology
gui
- 基于仿真平台开发下的蚁群算法，可以进行路径规划，寻找最优解-Based on the ant colony algorithm, under the simulation platform development path planning can be done, to find the optimal solution
colaborative-demo
- 基于协同稀疏表示的解混方法，因为每个端元的相似性，本文采用协同稀疏表示来约束每个像素采用相同位置系数不同的原子-Collaborative Sparse Regression for Hyperspectral Unmixing
SQSQ-REBUID
- 随机四参数构建多孔介质的三维模型，参数可灵活设置，程序算法精简-Construction of three-dimensional model of porous media random four parameters, the parameters can be flexibly set, streamlined program algorithm
HeatMap_test.m
- Matlab 代码 绘制 热力图像 能够通过视频中的动作来绘制热力图-Matlab Code for Heat Map plot Can plot a heat map based on movement in a video
RF_Reg_C
- 随机森林算法的matlab代码，这里旨在给初学者研究的随机树回归算法-Random forest algorithm Matlab code, aimed at beginners study a random tree algorithm
cells
- 用matlab对粘连的血红细胞进行分割并计数- Using matlab to adhesions red blood cells dividing and counting
TVAL3_beta2.4
- TVAL3算法是基于增广拉格朗日法和交替方向法的全变分最小化算法。TVAL3算法是在最小全变分法的基础上，结合了增强型拉格朗日函数和交替最小化方法。通过交替最小化方法寻找增强型拉格朗日模型的最小值，再由最速下降法进行迭代，更新拉格朗日乘子。-TVAL3: TV minimization by Augmented Lagrangian and ALternating direction ALgorithms.The TVAL3 algorithm is based on the minimu
BPFA_Denoise_04152010
- 非参贝叶斯字典学习用于图像去噪的matlab代码-BPFA denoising matlab code for the paper nonparametric bayesian dictionary learning for analysis of noisy and incomplete images download http://www.ee.duke/~mzl/Results/BPFAImage/.
GUI-(Tutorial-Version)
- 基于MATLAB的遥感图像融合，GUI界面，包括IHS，PCA，小波等等及他们的改进算法-MATLAB-based remote sensing image fusion, GUI interface, including IHS, PCA, wavelet, etc. and their improved algorithm
SRCNN
- 基于深度学习的图像超分辨率算法。 参考论文 Learning a Deep Convolutional Network for Image Super-Resolution (ECCV 2014) -Super resolution based on deep learning Refer to Learning a Deep Convolutional Network for Image Super-Resolution (ECCV 2014)