实验一

说明：  一个用于进行图像去噪的很好的算法，很新很好的一个算法(A well used for image denoising algorithm, an algorithm is a new good)

该代码采用IDL语言实现了从光谱库读取高光谱曲线，并且去除其包络。这是高光谱图像处理时一个重要的预处理技术。(The code using IDL language library to read from the spectrum of high spectral curve, and the removal of the envelope. It is time hyperspectral image processing is an important pre-processing techniques.)

说明：  线性判别分析（LDA）用于特征选择，可以对数据集或者图像提取有用特征，用于分类或者聚类等机器学习应用中(Linear Discriminant Analysis (LDA) for feature selection, application in dataset or image feature extraction, for classification or clustering applications in machine learning)

基于分数阶傅里叶变换的五篇博硕士论文，分别是在数字水印中的应用两篇，图象变换中的应用一篇，滤波中的应用一篇，目标检测中的应用一篇。(Based on Fractional Fourier Transform five Theses, respectively, in the digital watermark in the application of 2, images in the application of a transformation, filtering in the application of a target detection application 1.)

利用同态滤波对图像增晰 是matlab对图像处理的一中有效方法(Filters with exploitation Tai wave to increase Xi to the picture)

说明：  用delphi编程实现图像的颜色拾取，这是一个很好的图像处理程序(Delphi programming with the color of the image pick-up, this is a very good image processing program)

1、对观测数据进行中心化，； 2、使它的均值为0，对数据进行白化—>Z； 3、选择需要估计的分量的个数m，设置迭代次数p<-1 4、选择一个初始权矢量（随机的W，使其维数为Z的行向量个数）； 5、利用迭代W(i,p)=mean(z(i,:).*(tanh((temp) *z)))-(mean(1-(tanh((temp)) *z).^2)).*temp(i,1)来学习W （这个公式是用来逼近负熵的） 6、用对称正交法处理下W 7、归一化W(:,p)=W(:,p)/norm(W(:,p)) 8、若W不收敛，返回第5步 9、令p=p+1，若p小于等于m,返回第4步 剩下的应该都能看懂了 基本就是基于负熵最大的快速独立分量分析算法(1, on the center of the observation data, 2, making a mean of 0, the data to whitening-> Z 3, select the number of components to be estimated m, setting the number of iterations p < -1 4, select an initial weight vector (random W, so that the Z dimension of the row vectors of numbers) 5, the use of iteration W (i, p) = mean (z (i, :).* (tanh ((temp) ' * z)))- (mean (1- (tanh ((temp)) ' * z). ^ 2)).* temp (i, 1) to learn W (This formula is used to approximate the negative entropy) 6 with symmetric orthogonal treatments W 7, normalized W (:, p) = W (:, p)/norm (W (:, p)) 8, if W does not converge, return to step 5 9 , so that p = p+1, if p less than or equal m, return to step 4 should be able to read the rest of the basic is based on negative entropy of the largest fast independent component analysis algorithm)

一种使用牛顿迭代的超分辨算法，该算法以2的对数提高传统Landweber算法的运算速度，附带文章。(The problem of poor resolution of acquired image in the passive millimeter wave imaging stems mainly from antenna size limitations, thus necessitating efficient post-processing to achieve resolution improvements. A novel image super-resolution algorithm, Regularized-Newton-Residual (RNR) algorithm, is proposed for Passive Millimeter wave imaging (PMMW). First Newton inversion algorithm is introduced into the field of image restoration. The iterative scheme of Newton algorithm is deduced based on normal equation. Then, residual method is employed to achieve better performance and implement spectrum extrapolation. Experimental results demonstrate the algorithm is insensitive to noise distribution. Furthermore it has high super-resolution rate and fast convergence speed. )

利用ENVI/IDL，计算多幅图像的各个像元的平均值。并忽略空缺值(Use ENVI/IDL, calculate the average of each pixel of multiple images. And ignores the value of vacancies)

一幅大小为256×256的256级灰度图像（每像素8bit） 如果图像的各个像素之间是统计独立的，求该图像的信息熵 -离散无记忆信源 如果图像的各个像素之间不是统计独立的，求该图像的信息熵 -图像像素的概率分布模型(A 256 gray scale image (8bit per pixel) with a size of 256 x 256 If each pixel of the image is statistically independent, the information entropy of the image is calculated Discrete memoryless source If each pixel of the image is not statistically independent, the information entropy of the image is calculated Probability distribution model of image pixels)