matlab常用图像处理源码合集

demons三维非刚性图像配准，速度很快，基于ITK(demons of 3D non-rigid image registration, very fast, based on ITK)

这写程序是对加水印之后图像的攻击，包括加噪声，各种滤波，剪切，压缩攻击和旋转等。(This writing process is a plus watermark image after the attacks, including the increase in noise, various filtering, shear, compression and rotation attacks.)

图像处理中的深度学习，主要是原理的讲解，比较易懂,对于。(Image processing in deep learning, mainly to explain the principle, relatively easy to understand.)

极化SAR图像处理中，将协方差矩阵转换为STOKES矩阵或Mueller矩阵的程序(Polarization SAR image processing, the covariance matrix will be converted to matrix or Mueller matrix STOKES procedures)

guided_filter滤波器。对于图像处理方面有用。(filter. It is useful for image processing.)

提取图形的七阶不变矩，简单易行，利用求得特征矩进行图像的识别(It is simple and easy to extract the seven order moment of the graph, and the image is identified by using the characteristic moment.)

优化了的DLT算法，能够很好地跟踪目标。请下载woman等视频序列(Optimized DLT algorithm can track the target very well. Download woman and other video sequences)

自适应直方图均衡算法，对彩色图像的处理，有很好的效果。尤其是针对医学图像，内窥镜图像。(Adaptive histogram equalization algorithm for color image processing, have a good effect. Especially for medical images, endoscopic images.)

改进的聚类光流法，聚类方法有kmeans和DBSCAN，光流法金字塔LK可以用，HS光流法占用内存太多。(Improved clustering optical flow method, clustering method kmeans and DBSCAN, pyramid LK optical flow method can be used, HS optical flow occupy too much memory.)

为了提高数字图像中椭圆检测的效率和准确性,提出了一个基于最小二乘法的改进的随机椭圆检测算法. 该算法随机选取图像中的3 个边缘点,在以这3 个点为中心的窗口内,从边缘点中拟合出可能椭圆,并通过随机选 取的第4 个边缘点来确认可能椭圆. 利用直接最小二乘法椭圆拟合的特性,引入可能椭圆边缘点收集和椭圆重新 拟合的迭代过程来提取最终的椭圆参数. 通过对含有不同噪声的仿真图片和包括残缺椭圆的实际图片的实验表 明,新算法的改进是有效的. 与原算法相比,新算法降低了对参数的依赖性,提高了检测的速度、稳定性和准确性, 同时保留了原算法的抗噪声能力.(: An imp roved randomized ellip se detection algorit hm based on least square approach was proposed to enhance t he efficiency and accuracy of ellip se detection in digital images. This algorit hm randomly se2 lect s three edge point s in t he image , and t hen uses least square approach to fit all t he edge point s in three windows , which are defined by t he t hree edge point s. The forth edge point is randomly selected to judge whet her a possible ellip se exist s in t he image. Utilizing the characteristic of direct least square fit ting of ellip se , an iteration process of edge point collecting and ellip se refit ting of possible ellip se was int roduced to ext ract t he final ellip se’s parameters. Artificial images wit h different level s of noise and nature images containing incomplete ellip ses were employed to test t his algorithm. Experimental result s show that t he improvement s are notable. Compared with the original algorit hm , t he propo sed algorit hm reduces)