去噪声源代码 The Rudin-Osher-Fatemi total variation (TV) denoising technique poses the problem of denoising as a minimization, Min_u int |grad u|+ (lambda/2) int (f-u)^2 where f is the noisy image, lambda is a nonnegative parameter, and u is the denoised
Proceedings of the International Conference on PDE-Based Image Processing and Related Inverse problems,2005 by Xue-Cheng Tai (Editor), Knut-Andreas Lie (Editor), Tony F. Chan (Editor), Stanley Osher (Editor) This book publishes a collection of origi
Proceedings of the International Conference on PDE-Based Image Processing and Related Inverse problems,2005 by Xue-Cheng Tai (Editor), Knut-Andreas Lie (Editor), Tony F. Chan (Editor), Stanley Osher (Editor) This book publishes a collection of origi
Proceedings of the International Conference on PDE-Based Image Processing and Related Inverse problems,2005 by Xue-Cheng Tai (Editor), Knut-Andreas Lie (Editor), Tony F. Chan (Editor), Stanley Osher (Editor) This book publishes a collection of origi
分4 部分上传的，这是最后一部分 Proceedings of the International Conference on PDE-Based Image Processing and Related Inverse problems,2005 by Xue-Cheng Tai (Editor), Knut-Andreas Lie (Editor), Tony F. Chan (Editor), Stanley Osher (Editor) This book publishes a co
Contents Foreword vii Preface to the Second Edition xi Preface to the First Edition xv Guide to the Main Mathematical Concepts and Their Application xxv Notation and Symbols xxvii 1 Introduction 1 1.1 The Image Society.....................1 1.2 What
Level Set Methods and Dynamic Implicit Surfaces Authors: Stanley Osher, Ronald Fedkiw 这本书是创始人之一Osher写的，这本书是论述Level Set的最完整的书籍之一，更偏重于数值化的高精度解，应用领域涉及图像处理以及计算物理。
James A. Sethian. Level Set Methods and Fast Marching Methods. Cambridge University Press (1999). 评点：这是另外一个创始人Sethian的作品，与Osher的书互有侧重，互相补充，这本书更偏重于Fast Marching Methods, 非结构化网格，涉及的应用领域更广泛。
% u = TVDENOISE(f,lambda) denoises the input image f. The smaller % the parameter lambda, the stronger the denoising. % % The output u approximately minimizes the Rudin-Osher-Fatemi (ROF) % denoising model % % Min TV(u) + lambda/2 || f - u ||^2_2, %
可信任是AI应用落地的关键，在医疗、军事等领域尤为必需。最近，在一场深度学习与医疗应用的研讨会上，UCLA Stanley Osher讲述了可信任深度学习 (Trustworthy deep learning) 的报告，探讨了稳健、准确、高校、隐私的深度学习建模，从理论上如何设计保障，共有44页ppt，值得学习。
