hog-feature

说明：  在图像恢复技术中，总变分最小化是一种以保存图像细节为目标的规整化复原方法。本算法基于总变分最小化的思想，采取交替最小化的精确迭代策略来对图像和点扩展函数同时进行恢复。实验表明，该算法在噪声水平较高的情况下仍能对图像进行稳健的恢复。(Total variation image restoration techniques is a goal to save the image detail regularization recovery method. Algorithm based on total variation minimization thinking, take the alternating minimization accurate iteration strategy to simultaneously restore the image and point spread function. The experiments show that the algorithm in the case of high noise levels can still robust recovery image.)

实现非负ICA的盲源分离，可用于对图像等非负混合信号的分离(Non-negative ICA blind source separation, can be used for the separation of mixed-signal images and other non-negative)

理解频域处理与空间域处理的等效关系与步骤，掌握频域相乘与空间域循环卷积之间的对等关系，理解DFT运算中补零的效果。(Understand the equivalent relationship and steps of frequency domain processing and space domain processing, understand the reciprocal relationship between frequency domain multiplication and space domain circular convolution, and understand the effect of zero padding in DFT operation.)

求解分线性脉冲传输方程的SSFM方法,其中使用了改进的分步傅里叶算法，适合新手(the SSFM method to calculate the nonlinear pulse propagation equation)

基于RANSAC算法的图像匹配过程，包括提出该算法的文章以及文章中所提到的所有程序代码，可以实现具有高度异常点的图像匹配过程。(RANSAC algorithm based on image matching process, including all the program code of the algorithm proposed article and the article mentioned, the image matching process can be achieved with a high degree of abnormal points.)

RMSHE and AMSHE 可直接运行的直方图增强算法，附有相关的论文(RMSHE and AMSHE can be directly run by histogram enhancement algorithm, with related papers.)

基于小波多尺度算法的图像处理边缘检测 matlab(Algorithm based on wavelet multiscale edge detection image processing matlab)

说明：  在计算机视觉领域一种非盲去模糊的方法代码(A non-blind deblurring method code in the field of computer vision)

压缩感知compressed sensing ,剑桥大学 教授，可以用于降噪、图像处理、文件压缩等等；(Cambridge- Compressive Sensing- Theory and Application；)

使用hough变换检测图像中的线条；包括main函数和其他4个操作函数，有实验图片！(Detection using hough transform image lines including the main function and operation of the other four functions, there are experimental picture!)