LSH_MATLAB

说明：  XYZ视频图像压缩编码的应用原理与实际生活应用 (XYZ video image compression encoding)

c#编写的图像平滑处理，源码比较全，可以成功编译，希望对大家有帮助。(c# prepared by the image smoothing, source code comparison, can be successfully compiled, we want to help.)

高斯曲线的拟合可对照学习使用程序比较全面适合电磁波类的人员参考学习(Gaussian curve fitting may be learning to use the control procedures for a more comprehensive type of electromagnetic wave reference study)

说明：  标准三步搜索法，用matlab实现，经过实验效果还可以(Standard three-step search method, using matlab to achieve, after the experimental effect can also be)

采用MFC，实现遥感图像的基本增强，滤波，变换等功能。还可以保存数据。(Using MFC, to achieve the basic enhancement of remote sensing images, filtering, transformation and other functions. You can also save data)

数字图像处理，包括图像的卷积，相关，线性滤波处理，边缘检测都是运用labview来完成的。(Digital image processing, including images, linear convolution, filter, edge detection is accomplished using labview)

说明：  利用匹配滤波器实现图形识别，包括程序和说明文档(Graphic Recognition Using Matched Filter)

这是一个用matlab实现的基于支持向量机的人脸识别系统，已经本人调试通过，非常有用。(This is a matlab implementation with support vector machine-based face recognition system has been through my debugging, very useful.)

图像处理PDF格式的讲义，个人感觉不错，希望对大家有所帮助！(image processing PDF format overhead, personal feeling good, and I hope to help you!)

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.)