SusanCorner

说明：  阈值分割。基于二维直方图及混沌粒子群的图像分割方法(Thresholding. Based on two-dimensional histogram and chaotic particle swarm methods for image segmentation)

我写的基于bandelet域的spiht图像压缩算法。效果比经典的SPIHT好。文章已发，希望代码对大家有用。(I wrote bandelet domain-based SPIHT image compression algorithm. SPIHT is better than a good classic. Article has been distributed, I hope the code useful for everyone.)

极化SAR图像多纹理最大似然处理滤波算法，可以生成处理后的四个通道图像数据。(Multi-polarization SAR image texture filtering deal with maximum-likelihood algorithm, can be generated after the deal with the four-channel image data.)

图像的各向异性扩散，利用matlab实现Perona_Malik算法，梯度阈值K通过对图像求解2-范数的方法迭代获得(Anisotropic diffusion images, using matlab to achieve Perona_Malik algorithm, the gradient threshold images by solving the K 2- norm method will be obtained)

image processing with opencv

说明：  BP神经网络做曲线拟合，BP网络应用于曲线二次拟合(BP neural network to do curve fitting, BP network used in curve fitting quadratic)

本程序用于CT医学图像三维重建，使用MATLAB软件实现，简单易学(3d reconstruction usingmatlab)

实现简单图像处理，包括256色转灰度图、Hough变换、Walsh变换、中值滤波、二值化变换、亮度增减、傅立叶变换、反色、取对数、取指数、图像平移、图像旋转、图像细化、图像缩放、图像镜像、均值滤波、对比度拉伸、拉普拉斯锐化（边缘检测）、方块编码、梯度锐化、灰度均衡、直方图均衡、离散余弦变换、维纳滤波处理、逆滤波处理、阈值变换、高斯平滑。(simple image processing, including color to grayscale 256, the Hough transform, Walsh transform, Median filtering, binary conversion, brightness changes, Fourier transform, and color-from the few to admit index, image shifting, image rotation, refined images, image scaling, image mirroring, mean filtering, contrast stretching, Laplacian sharpening (edge detection), block codes, Sharpening gradient, gray balance, histogram equalization, discrete cosine transform, Wiener filter, reverse filtering, threshold transform, Gaussian smoothing.)

matlab回归法做图像变化检测。有图片有结果，有论文说明，可参照使用(Matlab regression method for image change detection. There are pictures of the results, there are papers, can refer to use)

基于改进的F-S error diffusion算法对彩色图像位数降为8色。其中分配系数采用动态系数(Based on the F-S error diffusion algorithm to improve the color image figures down to 8 colors. The distribution coefficient by using the dynamic coefficient)