给图像去噪，本程序通过全变差方法求解。程序简单 效果好

Inverse problems arise when we reconstruct a sharper image from a blurred one or reconstruct the underground mass density from measurements of the gravity above the ground. When we solve an inverse problem, we compute the source that gives rise to some observed data using a mathematical model for the relation between the source and the data. This book gives an introduction to the practical treatment of inverse problems by means of numerical methods, with a focus on basic mathematical and computational aspects. To solve inverse problems, we demonstrate that insight about them goes hand in hand with algorithms. Discrete Inverse Problems: Insight and Algorithms includes a number of tutorial exercises that give the reader hands-on experience with the methods, difficulties, and challenges associated with the treatment of inverse problems. It also includes examples and figures that illustrate the theory and algorithms. Audience This book is written for graduate students, researchers, and professionals in engineering and other areas that depend on solving inverse problems with noisy data. The aim is to provide readers with enough background that they can solve simple inverse problems and read more advanced literature on the subject. Contents Preface; List of Symbols; Chapter 1: Introduction and Motivation; Chapter 2: Meet the Fredholm Integral Equation of the First Kind; Chapter 3: Getting to Business: Discretizations of Linear Inverse Problems; Chapter 4: Computational Aspects: Regularization Methods; Chapter 5: Getting Serious: Choosing the Regularization Parameter; Chapter 6: Toward Real-World Problems: Iterative Regularization; Chapter 7: Regularization Methods at Work: Solving Real Problems; Chapter 8: Beyond the 2-Norm: The Use of Discrete Smoothing Norms; Appendix A: Linear Algebra Stuff; Appendix B: Symmetric Toeplitz-Plus-Hankel Matrices and the DCT; Appendix C: Early Work on 揟ikhonov Regularization? Bibliography; Index. About the Author Per Christian Hansen is Professor of Scientific Computing at the Technical University of Denmark. His publications include two other books on inverse problems, several MATLAB?packages, and many papers on inverse problems, matrix computations, and signal processing. His home page is http://www2.imm.dtu.dk/~pch/. To request an examination copy or desk copy of this book, please use our online request form at www.siam.org/catalog/adopt.php.

B样条函数拟合matlab代码

基于边界限制的去雾方法,与传统的暗原色不同,效果很好,大家可以和何凯明的方法对比

数据融合matlab代码通过具有非凸罚分的稀疏正则化进行图像融合 该源代码包包括用于所提出的图像融合和联合问题算法的MATLAB源代码。 纸： 每个主要代码的具体功能如下图所示： FL1：采用L1规范化正规化的图像融合； FGMC：通过GMC正则化进行图像融合； FSL1：采用L1规范正则化的联合图像融合和超分辨率； FSGMC：通过GMC正则化进行联合图像融合和超分辨率； FDrealGMC：在真正的OCT和眼底图像数据集（PSF估计）上使用GMC正则化进行联合图像融合和反卷积；

Regularization Tools Version 4.1

L2正则化原理： 过拟合的原理：在loss下降，进行拟合的过程中（斜线），不同的batch数据样本造成红色曲线的波动大，图中低点也就是过拟合，得到的红线点低于真实的黑线，也就是泛化更差。 可见，要想减小过拟合，减小这个波动，减少w的数值就能办到。 L2正则化训练的原理：在Loss中加入（乘以系数λ的）参数w的平方和，这样训练过程中就会抑制w的值，w的（绝对）值小，模型复杂度低，曲线平滑，过拟合程度低（奥卡姆剃刀），参考公式如下图： （正则化是不阻碍你去拟合曲线的，并不是所有参数都会被无脑抑制，实际上这是一个动态过程，是loss（cross_entropy）和L2 loss博弈的一个过程。训

主要介绍了tensorflow使用L2 regularization正则化修正overfitting过拟合方式，具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧

使用 Total Vairation 正则化进行图像去模糊。

MIT版深度学习第6章 深度学习的正则化 。 正则化的定义为旨在减少学习算法的泛化误差而不是训练误差的修改，训练神经网络中，常常会出现过拟合的问题