数字图像处理

matlab图像边缘边缘检测：包括小波多尺度边缘检测主程序、求纵向细节v和横行细节h的合成及模极大值、对矩阵取自适应阈值等(Matlab Edge Edge Detection : including wavelet multiscale edge detection main program. seeking details of the vertical and horizontal v h details of the synthesis and modulus maxima, the right matrix for adaptive threshold value)

本程序为基于快速双边滤波器的边缘保持算法，算法具有平移不变性，具有线性复杂度(this is a edge-preserving denoising method using fast bilateral filter)

高光谱图像的读取程序，在matlab界面直接调用就可以(Hyperspectral image reading program in matlab interface can be called directly)

题目： 八宝粥的生产过程中需要用到大量的红豆（红蚕豆），而实际上从红豆产地购买得到的红豆中会参杂一些杂物和不合格红豆。为了保证八宝粥的质量，需要对红豆进行筛选，去除不合格红豆和其它杂物。目前国内在红豆筛选工序上仍然使用人工筛选的方式，存在人工成本高、精确度差、劳动强度大等缺点。因此八宝粥生产厂商迫切需求开发一款能够自动实现红豆筛选的自动化筛选机。针对上述需求，请设计筛选机中的基于可见光图像的红豆筛选算法，该算法应尽可能的准确的找到参杂在红豆原料中的不合格红豆和杂物。 附件包括两个不同实验环境下拍摄得到的红豆相关图片样本库，实验环境分别为自然光照条件和灯光条件。每个条件下的样本库包括合格、不合格、杂物和混合四类样本的图像，其中合格、不合格以及杂物的图像可用来训练或建模，混合类图像用来测试算法的准确性和实时性。同学可在自然光照环境下的样本库或者灯光条件下的样本库中任选其一，用于解决上述问题。 作业提交格式包括以下四个内容： 一、 问题分析 二、 算法设计 三、 实验结果(将自己选择的样本库中的混合样本图片中的样本个体进行分类，并给出可视化的分类结果。) (Title:Use a lot of red bean production process of eight-treasure porridge ( red bean ), but in fact from the Red areas available in red bean can blend some sundries and unqualified red bean. In order to ensure the quality of eight-treasure porridge, the need for screening of red bean, red bean and other debris removal is not qualified. At present in the domestic bean screening process still use manual screening method, has the disadvantages of high labor costs, poor accuracy, high labor intensity. Therefore, eight-treasure porridge manufacturers need to develop a can achieve automatic bean screening automatic screening machine. According to the above requirements, please design screening machine selection algorithm based on visible light image of the red, the algorithm should be as accurate as possible to find the mixed in the bean raw materials in the unqualified red bean and debris.Accessories include captured in two different experimental environment of the red bean image sample dat)

将一张小图片影藏到大图片当中，使用半色调技术，并通过线性变换，调整两幅图像，将小图像影藏到大图像奇数行和列当中，可以调节线性函数的系数，改变影藏效果，改进后的(Will a small picture hidden to the big picture of a shadow of halftone use technology, and through the linear transformation, adjust the two images, the images hidden to big image of an odd number of rows and columns, can adjust the coefficients of the linear function, change the shadow hidden effect)

可以将连续图片转换成视频文件，值得参考学习(Convert a continuous image into a video file)

提出了一种广义迭代收缩算法（GISA）非凸稀疏编码，可以解决稀疏编码的一些优化问题.希望能对大家有所帮助！(A generalized iterative contraction algorithm (GISA) is proposed to solve some of the optimization problems of sparse coding. We hope to be helpful to everyone!)

本算法在Matlab2008b 环境下实现。包括main,san 和ssd 三个函数。 这次实现的算法并不是比较两个已经知道的点是否匹配，而是已知一个图形中的特征点，在另外一个图像中找到与其最匹配的点，匹配度用SAD 或者SSD 来度量。 main.m 是程序的入口，包括生成和读入实验数据，调用sad 函数和ssd 函数求匹配点，最后绘 制出最后的结果。sad.m 是用sad 度量方法在另一图中求解匹配点的函数实现，有3 个参数 y=sad(x,image1,image2)。其中x 是代匹配的数据，image1 是图像1 数据，image2 是图像2 数据。 从理论上分析，ssd 比sad 算法要复杂一点，经过测试，在一幅640*480 的图像中寻找10 个匹配点数据SAD 用时25.062519 秒，SSD 用时25.291432 秒。(The algorithm Matlab2008b environment to achieve. Including the main, san, and ssd three functions. The implementation of the algorithm is not the point of comparing two matches already know, but the known feature points in a graphic, an image found in the other match with the most points, matching measured with the SAD or SSD. main.m is the entry, including the generation and reading test data, call the sad ssd function evaluation functions and matching points, and finally draw the final result. sad.m measure is sad figure in another match point in the function implementation to solve, there are three parameters y = sad (x, image1, image2). Where x is the generation of matching data, image1 is the data image 1, image2 is the image 2 data. From the theoretical analysis, ssd little more complicated than the sad algorithm, tested in a 640* 480 images of 10 match points in the search for data using time 25.062519 seconds SAD, SSD with time 25.291432 seconds. )

可以实现小目标的检测 ，用matlab语音编写 。(Small targets can be detected, written in MATLAB.)

说明：  用PALKING模糊方法进行边缘检测，还有3个自适应改进方法(PALKING fuzzy method with edge detection, there are three adaptive Improvement)