论文Deep_Convolutional_Neural_Network_for_Inverse_Problems_in_Imagin
2022-12-06 17:26:37 20.93MB CT图像重建 CT算法研究 论文
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This textbook evolved from a course in geophysical inverse methods taught during the past two decades at New Mexico Tech, first by Rick Aster and, subsequently, jointly between Rick Aster and Brian Borchers. The audience for the course has included a broad range of first- or second-year graduate students (and occasionally advanced under- graduates) from geophysics, hydrology, mathematics, astrophysics, and other disciplines. Cliff Thurber joined this collaboration during the production of the first edition and has taught a similar course at the University of Wisconsin-Madison. Our principal goal for this text is to promote fundamental understanding of param- eter estimation and inverse problem philosophy and methodology, specifically regarding such key issues as uncertainty, ill-posedness, regularization, bias, and resolution. We emphasize theoretical points with illustrative examples, and MATLAB codes that imple- ment these examples are provided on a companion website. Throughout the examples and exercises, a web icon indicates that there is additional material on the website. Exercises include a mix of applied and theoretical problems. This book has necessarily had to distill a tremendous body of mathematics and science going back to (at least) Newton and Gauss. We hope that it will continue to find a broad audience of students and professionals interested in the general problem of estimating physical models from data. Because this is an introductory text surveying a very broad field, we have not been able to go into great depth. However, each chapter has a “notes and further reading” section to help guide the reader to further explo- ration of specific topics. Where appropriate, we have also directly referenced research contributions to the field. Some advanced topics have been deliberately left out of this book because of space limitations and/or because we expect that many readers would not be sufficiently famil- iar with the required mathematics. For example, readers with a strong mathematical background may be surprised that we primarily consider inverse problems with discrete data and discretized models. By doing this we avoid much of the technical complexity of functional analysis. Some advanced applications and topics that we have omitted include inverse scattering problems, seismic diffraction tomography, wavelets, data assimilation, simulated annealing, and expectation maximization methods. We expect that readers of this book will have prior familiarity with calculus, dif- ferential equations, linear algebra, probability, and statistics at the undergraduate level. In our experience, many students can benefit from at least a review of these topics, and we commonly spend the first two to three weeks of the course reviewing material from
2022-10-15 15:36:14 6.14MB inverse problems
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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.
2022-08-09 09:33:04 3.59MB Inverse problems regularization parameter
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OpenEIT仪表板 生物医学成像以前很昂贵,几乎无法破解和试验。 如果有更多的人进行实验并了解成像的工作原理,我们可以更快地将其向前发展,并使这些变革性技术对所有人开放。 OpenEIT(EIT用于电阻抗层析成像)使用与CATSCAN相同的层析成像重建技术,使用非电离交流电流来重建任何导电材料(例如,肺部,手臂或头部)的图像。 PCB只有2英寸见方的正方形,带有蓝牙,使之成为进行生物医学成像的便携式且易于破解的方式! WINDOWS用户注意事项 SPECTRA使用FTDI芯片通过UART进行通信。 VCP FTDI驱动程序未预安装在Windows上(但已安装在所有其他操作系统上)。 如果您运行的是Windows计算机,则应按照以下说明安装FTDI驱动程序,然后再继续进行仪表板安装: : 如何安装python仪表板。 要求 Python 3.6.7 安装 pip install -r
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电子书:Introduction to Inverse problems in imaging,关于图像处理中的逆问题
2022-01-27 10:31:09 3.36MB 图像处理
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COMPUTATIONAL METHODS FOR INVERSE PROBLEMS,反问题的计算方法,国外艾斯维尔出版的关于反问题求解的重要书籍,JPG版本
2021-11-25 14:45:55 8.14MB INVERSE PROBLEMS
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描述 该项目旨在消除源自手持摄像机运动或抖动的运动模糊。 它旨在盲目工作,即不需要模糊知识。 使用卷积神经网络估计运动模糊,然后将其用于校准反卷积算法。 该项目包括两个不同的部分: -图像处理部分,包括反卷积算法和正向模型。 -使用神经网络的模糊估计部分。 有关某些视觉见解,请参见 。 该库使用Python3编码。 无论是在图像处理(复杂模糊的建模)还是在模糊估计方面,其贡献都倍受欢迎。 消息 从2020年5月开始,该项目重新启动! 我们从tensorflow转到pytorch。 我们将把运动模糊模型扩展到比简单的线性运动更复杂的运动。 我们还将解决空间变异情况。 我们计划扩展到电视去模糊。 进步 截至目前(2020年5月),我们支持使用Wiener滤波器对线性模糊进行模糊处理。 安装 在您喜欢的conda环境中,键入: pip install -e . 为了进行开发,请按
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逆问题是几乎所有遥感探测的数学原理,诸如医学成像、地震探测、雷达成像,超声探测等。掌握了逆问题求解方法,也就掌握了不同探测模式的共同本质。
2021-10-13 22:08:16 7.97MB 逆问题 信号处理 计算方法
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7.5 聚类法 思路 将像素投射到特征空间成为样本点,根据样本点在特征空间的分布特性进行聚类。将类别标号投射回图像空间作为 像素的标号,进而实现分割。 哪些视觉元素容易被聚为同一类(1F2S2P4C) Proximity : 空间相邻性 Similarity : 特征相似性 Common fate : 运动同向性 Common region : 区域归属 Closure : 趋向于闭合 Parallelism : 平行性 Symmetry : 对称性 Continuity : 连续性 Familiar pattern : 组合后的熟悉程度 代表性的聚类分割算法 合成聚类与分裂聚类 每个样本点作为一个独立的簇;将所有样本作为一个簇 K-means 算法 模糊 C 均值聚类 Meanshift 算法 SLIC 超像素 K-means 的基本思想 将图像中所有的元素视为来源于 k 个类别,根据样本到类别中心的特征距离判断像素的归属,通过迭代更新的方式 在逼近类别模型参数的同时实现像素的分类。 K-means 的步骤 1. 为像素选择特征向量(比如 YUV 色彩特征),将所有像素映射为特征空间中的样本点。 2. 选择类别数量 k,在特征空间随机初始化 k个类的中心。 3. 根据样本点到类中心的距离,为每一个样本点选择距离最近类作为类别标号 4. 根据新的分类结果,以同一类样本点的特征均值更新类中心。 5. 重复步骤 3-4, 直到类中心的位置不再发生变化。
2021-10-12 10:46:48 1.76MB 数字图像处理
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