meng_cv

基于VC的各种火焰真实模拟,有效的显示了火焰的效果.(fire)

分析了合成孔径雷达的模糊度问题及其对图像质量的影响, 详细描述了模糊度的 表达式, 分析了不同系统参数对模糊度的影响, 对所谓的重影现象作了定量分析(Analysis of synthetic aperture radar ambiguity problems and their impact on image quality, a detailed description of the fuzzy degree of expression of the different system parameters on the degree of influence on the so-called ghost phenomenon analyzed quantitatively)

数字图象处理VC++实现,里面有很多图像处理方面的算法，有参考意义。(Digital image processing in VC++)

c# 底片效果显示图像,就像旧式相机的底片一样

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.)

a lane dtection method using hough-transform

计算多边形面积的算法(polygon area calculation algorithm)

利用turbopixel的超像素图像分割方法，可以显示不同块的颜色。(This is a mex c and matlab code, which uses the turbopixel to segment the object from the image. )

基于OpenCV实现的图像切割程序，通过程序中参数设置分块的数量和大小(The project is uesd to break an image into block)

说明：  求取图像信噪比 均方差 峰值信噪比 逼真度等的matlab 程序函数(Average Absolute Difference，Signal to Noise Ratio (dB)，Peak Signal to Noise Ratio (dB)，Image Fidelity，Mean Square Error，The difference between restoredimg entroy and originalimg，entroy)