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基于图像特征的模板匹配算法的研究， 进行图像处理地子程序；(Template based on image feature matching algorithm for image processing to Subroutine )

图象分割中应用较多的ostu大津阈值法,matlab源代码(Application of image segmentation ostu more Otsu threshold method, matlab source code)

对图像进行镜像处理，是最基本的图像处理操作(Mirror image is the most basic image processing operations )

实现ICM算法（条件迭代模型）对于灰度图像的分割(Achieve ICM algorithm (iterative model conditions) for the gray-scale image segmentation)

图像清晰度评价，包含标准差，空间频阈，熵，bener(Image sharpness evaluation, including standard deviation, spatial frequency threshold, entropy, bener)

改进的分水岭算法，利用基于重建的开闭算法，有效的解决了过分割问题，尤其适用与医疗血细胞的分割问题(Improved watershed algorithm, based on the reconstruction of the opening and closing algorithm, effectively resolved the issue of over-segmentation, in particular with the medical application of the division of blood cells)

机器视觉之模糊图像复原，实现inverse Filter, Wiener Filter, Lucy-richardson 算法，里面包括PPT，代码和报告。非常有价值，值得参考！ (Machine vision blurred image restoration, achieve The inverse Filter, Wiener Filter, Lucy-richardson algorithm, which includes the PPT, the code and report. Very valuable and worth considering!)

边缘检测算法的比较，有susan算子，log算子，prewitt算子(Comparison of edge detection algorithm, there is susan operator, log operator, prewitt operator)

说明：  matlab图像滤波去噪分析及其应用 双线性滤波、Kirsch滤波、超限邻域滤波、逆滤波、双边滤波、同态滤波、小波滤波、六抽头滤波、约束最小平方滤波、非线性复扩散滤波、Lee滤波、Gabor滤波，、Wiener滤波、Kuwahara滤波、Beltrami流滤波、Lucy?Richardson滤波、NoLocalMeans滤波等研究内容。(Analysis and application of MATLAB image filtering and denoising Bilinear filtering, Kirsch filtering, transfinite neighborhood filtering, inverse filtering, bilateral filtering, homomorphic filtering, wavelet filtering, six tap filtering, constrained least square filtering, nonlinear complex diffusion filtering, Lee filtering, Gabor filtering, Wiener filtering, Kuwahara filtering, Beltrami flow filtering, Lucy Richardson filtering, nolocalmeans filtering, etc.)

基于TV正则化算法的图像去噪重构，包含有论文说明，及代码实例(Reconstruction based on TV regularization algorithm for image denoising，containing the paper description, and examples)