K-means代码

说明：  提取图像的hu矩 使用matlab编写 相当稳定 计算速度较快(image hu moment steadily run fast very good worth trying)

《基于计算机视觉的普适性三维重建方法研究》源代码，实现了一种将二维图像重建成三维场景的方法。(Source code of <General Sense Three-Dimension Reconstruction Research Based on Computer Vision>, a technique which can convert 2D images into 3D sence.)

压缩感知中稀疏分解算法的正交匹配追踪算法OMP(An orthogonal matching pursuit algorithm for compressed sensing OMP)

基于DCT,DFT,DWT等的水印算法，还有加置乱的水印算法，用于图像处理方面(Based on the DCT, DFT, DWT, such as the watermarking algorithm, as well as increase the watermark scrambling algorithm, for image processing)

GS算法，高斯光束整形成矩形光斑，或者根据自制的图片进行逆推相位(GS algorithm, Gaussian beam shaping)

Matlab的GUI中选择图像的感兴趣区域ROI(正方形和矩形)(Matlab GUI to select the image region of interest ROI (square and rectangular))

图像纹理分析的源代码，基于分形维数的图像纹理分析(Image texture analysis of source code, based on fractal dimension of image texture analysis)

基于opencv实时图像的2值化处理，一定要有摄像头(Opencv-based real-time image processing of two values, there must be a camera)

说明：  经典的基于非局部思想的图形图像去噪模型，也即非局部滤波，基于matlab实现(The classical image denoising model based on nonlocal idea, namely non local filtering, is realized based on MATLAB)

提出了一种结合USFFT Curvelet 变换的各向异性扩散图像去噪模型。它有机结合了Curvelet 变换和各向 异性扩散（P-M扩散）两者的优点。通过P-范数方法选择合适的梯度阈值K，P-M扩散过程通过处理经过Curvelet 变换得到的图像的不同尺度的Curvelet 系数矩阵，实现了建立在对图像多尺度分析的基础上的新P-M扩散模型。 实验表明，新模型的处理结果能有效避免传统P-M 扩散出现的阶梯效应，同时更好地保留图像的纹理和细节。 关键词：图像去噪算法；各向异性扩散；Curvelet 变换；P-M 扩散(Abstract: An image de-noising model by integrating anisotropic diffusion with USFFT Curvelet transform was proposed, which combined the strongpoint of Curvelet transform with anisotropic diffusion (P-M diffusion). By choosing appropriate gradient threshold K through p-norms and carrying out the P-M diffusion process depend on the different scale matrixes of Curvelet coefficient of the image from Curvelet transform iterations, as a result, the improved model made it possible to carry out the new P-M diffusion de-noising process based on multi-scale analysis of the image. The experiment results have demonstrated that the model can avert the stair-casing effect in the traditional P-M diffusion effectively and keep the textures and details of images better.)