radiometric-correction-

说明：  分析了如何利用小波变换技术对图像进行信息融合使读者从整体上对医学图像融合技术有一个比较清楚直观的理解 (Analysis of how to use wavelet transform to the image information fusion, so that readers from the whole of the medical image fusion technology has a relatively clear and intuitive understanding.)

用于植物叶片分类的图形界面,是Matlab源码，用BP神经网络实现(For plant leaf classification graphical interface, the Matlab source code, using BP neural network)

对两帧图像进行ICM调试.包含内容：1估计姿态和摄像机内参数；2两幅图做四次金字塔；3利用输入特征点采用ransac算法估计姿态R, T。4sift特征匹配；5摄像机标定。(ICM images on two debugging. Contains: 1 Estimated posture and intrinsic parameters 2 two plans to do four pyramids 3 using the input feature points using ransac algorithm estimates gesture R, T. 4sift feature matching 5 camera calibration.)

基于Matlab的函数优化遗传算法程序,包含了遗传算法实现的大部分步骤(Based on Matlab function optimization of genetic algorithm, contains most of the genetic algorithm implementation steps)

对火焰图像进行二值化 边缘检测 链码提取 并计算火焰图像的面积和周长 绝对好用！(Binarization flame image edge detection chain code extraction and absolutely easy to calculate the area and perimeter of the flame image!)

生成相机标定所用的方格图，用于相机标定使用(Generates the grid chart used to calibrate the camera)

说明：  matlab编写计算直边图像的点扩散函数PSF(Compiling Point Spread Function ( PSF) for Computing Straight Edge Images with MATLAB)

稀疏表示，包括ksvd图像去噪，matlab程序，程序有详细注解，便于初学者学习，代码可以直接使用(Sparse representation, including ksvd image denoising， Matlab procedures, procedures are detailed notes, easy for beginners to learn, code can be used directly)

利用压缩感知实现波达方向估计，运用奇异值分解对接收信号进行降维，再利用L1范数进行估计(Using compressed sensing DOA estimation using singular value decomposition to reduce the dimensionality of the received signal, and then using the L1 norm estimate)

说明：  Donoho提出的小波阀值去噪的基本思想是将信号通过小波变换（采用Mallat算法）后，信号产生的小波系数含有信号的重要信息，将信号经小波分解后小波系数较大，噪声的小波系数较小，并且噪声的小波系数要小于信号的小波系数，通过选取一个合适的阀值，大于阀值的小波系数被认为是有信号产生的，应予以保留，小于阀值的则认为是噪声产生的，置为零从而达到去噪的目的。尺度不变特征变换（Scale-invariant feature transform，SIFT），是用于图像处理领域的一种描述。这种描述具有尺度不变性，可在图像中检测出关键点。(The basic idea of wavelet threshold de-noising proposed by Donoho is that the wavelet coefficients generated by the signal after passing through the wavelet transform (Mallat algorithm) contain the important information of the signal. After the wavelet decomposition, the wavelet coefficients of the signal are larger, the wavelet coefficients of the noise are smaller, and the wavelet coefficients of the noise are smaller than the wavelet coefficients of the signal. By selecting a suitable threshold, the wavelet coefficients of the noise are larger than the valve The wavelet coefficients of values are considered to be generated by signals, which should be preserved. The ones less than the threshold are considered to be generated by noise, which is set to zero to achieve the purpose of denoising.)