hongmo2

最邻近插值的matlab实现代码，可以对图像进行插值，插值的方法为最邻近插值法(The nearest neighbor interpolation matlab code, image interpolation, interpolation methods nearest neighbor interpolation)

用来对2维信号的频域进行sinc插值处理，以放大图像。插值的结果较为理想(Used for 2-D frequency domain signal processing to sinc interpolation in order to enlarge the image. Interpolation more satisfactory results)

在Matlab平台下实现人脸检测的一个程序(In Matlab platform to achieve a face detection process)

本程序能够实现图像处理中的图像细化，是基于matlab的。(This procedure can realize image processing image thinning is based on the matlab.)

图像加密 matlab程序 图像加密的程序 混沌系统图像加密的程序(encrypt photo)

（1） 进行小波的图像消噪处理 （2） 进行小波的图像压缩处理 （3） 进行小波的图像增强处理 ((1) Wavelet Image Denoising (2) Wavelet Image Compression (3) Wavelet Image Enhancement)

说明：  对一副已经给出的图像进行中值滤波处理，利用罗伯特算子、索伯尔算子、canny算子、拉普拉斯算子，分别进行边缘检测。 具体见说明文档。(A given image is processed by median filter. Robert operator, Sobel operator, Canny operator and Laplace operator are used for edge detection. See the instruction document for details.)

基于模拟退火与马尔可夫随机场的遥感图像分割的matlab代码(the image segmentation based on the SA and MNF)

图像增强，包括图像的平滑、锐化、边缘识别等操作(Image enhancement, including image smoothing, sharpening, edge detection and other operations)

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)