将高光谱数据转换换成对应卫星传感器（Landsat_OLI、WorldView2、Sentinel A）的多光谱数据

Lecture note spectra of PDE

Matlab代码sqrt 频谱杂波 一套有用的工具，用于处理振动（拉曼/ SERS / IR）或其他类似的光谱。 基线校正 baseline.mor.R 一种基于形态学运算（MOR）的方法和一种基于迭代形态学运算的全自动方法（I-MOR）。 I-MOR在“现实世界”光谱中显示出良好的结果。 但是，输出基线具有类似楼梯的形状，可能会损害频谱信息。 MOR和I-MOR代码都是C ++增强的，这使它们相当快。 参考： 戴联奎和陈云亮。 EXPRESS：一种基于迭代形态学操作的自动基线校正方法。 DOI：10.1177 / 0003702817752371 baseline.Goldinec.R 基于成本函数回归的Goldindec半自动基线校正方法。 该函数的代码经过了优化，并转换为原始Matlab代码的版本（请参见参考资料）。 该函数已经过测试，可提供与原始Matlab代码相同的输出。 但是，在“现实世界”光谱中，该方法的性能并不比多项式好得多。 参考： 刘俊涛，孙建阳，黄秀珍，李国军，刘斌强。 Goldindec：拉曼光谱基线校正的新算法。 DOI：10.1366 / 14-07798

打印RIP-TilePrint Spectra RIP印染瓷片打印控制软件2017版.docx

Spectra of Graphs （图论）Andries E. Brouwer • Willem H. Haemers

Matlab 函数 espectra.m 使用发射线波长和线强度表绘制可见发射线光谱。 它基本上是 John Talbot 1997 年由互联网上的多个站点托管的开源 Java 程序 dicharge.java 的 Matlab 版本。 此功能包括几乎所有的光谱数据从氢到铀的元素。 有关详细信息，请参阅 readme.txt 文件。

一本学习image deblurring的很好的入门教材，DjVu格式的； Deblurring Images: Matrices, Spectra, and Filtering (Fundamentals of Algorithms 3) (Fundamentals of Algorithms) By Per Christian Hansen Publisher: Society for Industrial and Applied Mathematic Number Of Pages: 130 Publication Date: 2006-10-29 ISBN-10 / ASIN: 0898716187 ISBN-13 / EAN: 9780898716184

【作 者】Per Christian Hansen 【出版社】Society for Industrial and Applied Mathematic 【出版日期】October 29, 2006 【ISBN】0898716187 9780898716184 【形态项】9.8 x 6.7 x 0.3 inches 【语 言】English 【价 格】$63.00 Deblurring Images: Matrices, Spectra, and Filtering (Fundamentals of Algorithms 3) (Fundamentals of Algorithms) By Per Christian Hansen Publisher: Society for Industrial and Applied Mathematic Number Of Pages: 130 Publication Date: 2006-10-29 ISBN-10 / ASIN: 0898716187 ISBN-13 / EAN: 9780898716184 Binding: Paperback “The book’s focus on imaging problems is very unique among the competing books on inverse and ill-posed problems. …It gives a nice introduction into the MATLAB world of images and deblurring problems.” — Martin Hanke, Professor, Institut für Mathematik, Johannes-Gutenberg-Universität. When we use a camera, we want the recorded image to be a faithful representation of the scene that we see, but every image is more or less blurry. In image deblurring, the goal is to recover the original, sharp image by using a mathematical model of the blurring process. The key issue is that some information on the lost details is indeed present in the blurred image, but this “hidden” information can be recovered only if we know the details of the blurring process. Deblurring Images: Matrices, Spectra, and Filtering describes the deblurring algorithms and techniques collectively known as spectral filtering methods, in which the singular value decomposition—or a similar decomposition with spectral properties—is used to introduce the necessary regularization or filtering in the reconstructed image. The concise MATLAB® implementations described in the book provide a template of techniques that can be used to restore blurred images from many applications. This book’s treatment of image deblurring is unique in two ways: it includes algorithmic and implementation details; and by keeping the formulations in terms of matrices, vectors, and matrix computations, it makes the material access

经典书籍：An Introduction to the Theory of Graph Spectra PDF版