《深度学习与图像复原》田春伟

如何自学 占星术 占星教程网盘 塔罗牌教程百度网盘

《深度学习与图像复原》田春伟【文字版_PDF电子书_】

内容简介：

随着数字技术的飞速发展，图像已成为一种至关重要的信息载体，无论是社交媒体上的图像分享、新闻报道中的图像应用，还是医疗领域的图像分析，数字图像都以其独特的直观性和高效性广泛渗透于人们日常生活的诸多领域。然而，图像质量往往受到相机晃动、噪声干扰和光照不足等多种因素的影响，这给的图像分析带来了巨大挑战。图像复原技术可以消除受损图像中的干扰信号，并重构高质量图像。为此，本书深入剖析了图像复原技术的进展，并探索了深度学习技术在图像复原过程中的关键作用。本书集理论、技术、实践于一体，不仅可以为相关领域的学者和学生提供宝贵的学术资源，还可以为工业界的专业人士提供利用先进技术解决实际问题的方法。本书面向对深度学习与图像复原知识有兴趣的爱好者及高校相关专业学生，期望读者能有所收获。

作者简介：

田春伟，西北工业大学副教授、博士生导师。空天地海一体化大数据应用技术国家工程实验室成员。入选2023和2022年全球前2%*尖科学家榜单、省级人才、市级人才、西北工业大学翱翔新星。研究方向为视频/图像复原和识别、图像生成等。在国际期刊和国际会议上发表论文70余篇，其中6篇ESI高被引论文、3篇ESI热点论文、4篇顶刊封面论文、5篇国际超分辨领域Benchmark List论文、3篇GitHub 2020具有贡献代码，1篇论文技术被美国医学影像公司购买商用，1篇论文技术被日本工程师应用于苹果手机上等。

目  录：

第1 章 基于传统机器学习的图像复原方法 ……………………………………………………. 1

1.1 图像去噪 ···············································································1

1.1.1 图像去噪任务简介···························································1

1.1.2 基于传统机器学习的图像去噪方法 ·····································1

1.2 图像超分辨率 ·········································································9

1.2.1 图像超分辨率任务简介 ····················································9

1.2.2 基于传统机器学习的图像超分辨率方法 ·······························9

1.3 图像去水印 ·········································································.15

1.3.1 图像去水印任务简介 ····················································.15

1.3.2 基于传统机器学习的图像去水印方法 ·······························.15

1.4 本章小结 ············································································.19

参考文献 ···················································································.20

第2 章 基于卷积神经网络的图像复原方法基础 …………………………………………… 24

2.1 卷积层 ···············································································.24

2.1.1 卷积操作 ····································································.26

2.1.2 感受野 ·······································································.29

2.1.3 多通道卷积和多卷积核卷积 ···········································.30

2.1.4 空洞卷积 ····································································.31

2.2 激活层 ···············································································.33

2.2.1 Sigmoid 激活函数 ·························································.33

2.2.2 Softmax 激活函数 ·························································.35

2.2.3 ReLU 激活函数 ···························································.36

2.2.4 Leaky ReLU 激活函数 ···················································.38

2.3 基于卷积神经网络的图像去噪方法 ···········································.39

2.3.1 研究背景 ····································································.39

2.3.2 网络结构 ····································································.40

2.3.3 实验结果 ····································································.42

2.3.4 研究意义 ····································································.47

2.4 基于卷积神经网络的图像超分辨率方法 ·····································.48

2.4.1 研究背景 ····································································.48

2.4.2 网络结构 ····································································.48

2.4.3 实验结果 ····································································.51

2.4.4 研究意义 ····································································.55

2.5 基于卷积神经网络的图像去水印方法 ········································.55

2.5.1 研究背景 ····································································.55

2.5.2 网络结构 ····································································.56

2.5.3 实验结果 ····································································.58

2.5.4 研究意义 ····································································.61

2.6 本章小结 ············································································.62

参考文献 ···················································································.62

第3 章 基于双路径卷积神经网络的图像去噪方法 ……………………………………….. 69

3.1 引言 ··················································································.69

3.2 相关技术 ············································································.70

3.2.1 空洞卷积技术 ······························································.70

3.2.2 残差学习技术 ······························································.71

3.3 面向图像去噪的双路径卷积神经网络 ········································.72

3.3.1 网络结构 ····································································.72

3.3.2 损失函数 ····································································.74

3.3.3 重归一化技术、空洞卷积技术和残差学习技术的结合利用 ····.74

3.4 实验结果与分析 ···································································.76

3.4.1 实验设置 ····································································.77

3.4.2 关键技术的合理性和有效性验证 ·····································.79

3.4.3 灰度与彩色高斯噪声图像去噪 ········································.83

3.4.4 真实噪声图像去噪························································.87

3.4.5 去噪网络的复杂度及运行时间 ········································.89

3.5 本章小结 ············································································.89

参考文献 ···················································································.90

第4 章 基于注意力引导去噪卷积神经网络的图像去噪方法 …………………………. 93

4.1 引言 ··················································································.93

4.2 注意力方法介绍 ···································································.94

4.3 面向图像去噪的注意力引导去噪卷积神经网络 ···························.94

4.3.1 网络结构 ····································································.95

4.3.2 损失函数 ····································································.96

4.3.3 稀疏机制和特征增强机制 ··············································.96

4.3.4 注意力机制和重构机制 ·················································.98

4.4 实验与分析 ·········································································.99

4.4.1 实验设置 ····································································.99

4.4.2 稀疏机制的合理性和有效性验证 ···································.100

4.4.3 特征增强机制和注意力机制的合理性和有效性验证 ···········.102

4.4.4 定量和定性分析 ·························································.103

4.5 本章小结 ···········································································.110

参考文献 ··················································································.110

第5 章 基于级联卷积神经网络的图像超分辨率方法 ………………………………….. 114

5.1 引言 ·················································································.114

5.2 相关技术 ···········································································.115

5.2.1 基于级联结构的深度卷积神经网络 ·································.115

5.2.2 基于模块深度卷积神经网络的图像超分辨率 ·····················.116

5.3 面向图像超分辨率的模块深度卷积神经网络 ······························.117

5.3.1 网络结构 ···································································.118

5.3.3 低频结构信息增强机制 ················································.119

5.3.4 信息提纯块 ·······························································.120

5.3.5 与主流网络的相关性分析 ············································.121

5.4 实验与分析 ·······································································.123

5.4.1 实验设置 ··································································.123

5.4.2 特征提取块和增强块的合理性和有效性验证 ····················.124

5.4.3 构造块和特征细化块的合理性和有效性验证 ····················.126

5.4.4 定量和定性估计 ·························································.127

5.5 本章小结 ··········································································.135

参考文献 ·················································································.136

第6 章 基于异构组卷积神经网络的图像超分辨率方法 ………………………………. 142

6.1 引言 ················································································.142

6.2 相关技术 ··········································································.143

6.2.1 基于结构特征增强的图像超分辨率方法 ··························.143

6.2.2 基于通道增强的图像超分辨率方法 ································.144

6.3 面向图像超分辨率的异构组卷积神经网络 ································.145

6.3.1 网络结构 ··································································.145

6.3.2 损失函数 ··································································.147

6.3.3 异构组块 ··································································.148

6.3.4 多水平增强机制 ·························································.149

6.3.5 并行上采样机制 ·························································.150

6.4 实验结果与分析 ·································································.155

6.4.1 数据集 ·····································································.155

6.4.2 实验设置 ··································································.155

6.4.3 方法分析 ··································································.156

6.4.4 实验结果 ··································································.157

6.5 本章小结 ··········································································.166

参考文献 ·················································································.166

第7 章 基于自监督学习的图像去水印方法 ………………………………………………… 173

7.1 引言 ················································································.173

7.2 自监督学习 ·······································································.174

7.2.1 卷积神经网络 ····························································.175

7.2.2 生成对抗网络 ····························································.176

7.2.3 注意力机制 ·······························································.176

7.2.4 混合模型 ··································································.176

7.3 面向图像去水印的自监督学习方法 ·········································.177

7.3.1 基于自监督卷积神经网络的结构 ···································.177

7.3.2 异构网络 ··································································.178

7.3.3 感知网络 ··································································.179

7.3.4 损失函数 ··································································.179

7.4 实验结果与分析 ·································································.180

7.4.1 数据集 ·····································································.180

7.4.2 实验设置 ··································································.180

7.4.3 方法分析 ··································································.181

7.4.4 实验结果 ··································································.184

7.5 本章小结 ··········································································.189

参考文献 ·················································································.189

第8 章 总结与展望 …………………………………………………………………………………… 195

8.1 总结 ················································································.195

8.2 展望 ················································································.197

致谢 …………………………………………………………………………………………………………….. 198

浏览器不支持脚本！

图像复原 深度学习 卷积神经网络

摘要：围绕田春伟所著《深度学习与图像复原》一书，本文系统梳理了该著作在理论体系、技术方法、应用价值以及学术意义等方面的核心内容。文章首先从整体视角概述全书的研究背景与写作宗旨，指出其在深度学习与图像处理交叉领域中的重要地位。随后，正文从四个关键方面展开深入阐述：一是图像复原问题的理论基础与发展脉络，二是深度学习模型在图像复原中的方法创新，三是典型应用场景与实验验证，四是对相关领域未来研究的启示与拓展价值。通过多层次、多角度的分析，本文力图呈现《深度学习与图像复原》在学术深度与实践指导上的双重价值，帮助读者全面理解该著作的思想精髓与现实意义。

1、图像复原理论基础

《深度学习与图像复原》在开篇部分系统回顾了图像复原问题的起源与发展历程。田春伟从经典的信号处理理论入手，阐述了噪声模型、退化模型以及逆问题在图像处理中的核心地位，使读者能够清晰理解图像复原研究的理论根基。

在理论阐释过程中，作者重点分析了传统图像复原方法的优势与局限，例如基于滤波、正则化和统计建模的方法。这些内容不仅帮助读者建立完整的知识框架，也为后续引入深度学习方法奠定了坚实的对比基础。

此外，书中还对图像复原的数学建模问题进行了深入讨论，包括病态问题、先验假设以及优化求解策略等关键概念。这种由浅入深的理论铺陈，使得复杂问题具备了清晰的逻辑脉络。

2、深度学习方法创新

在深度学习方法的介绍中，田春伟重点阐述了卷积神经网络在图像复原任务中的核心作用。作者通过结构化的讲解，使读者能够理解网络层次设计、特征提取机制以及非线性映射能力对复原质量的影响。

书中进一步探讨了多种先进网络结构，如残差网络、生成对抗网络以及注意力机制在图像复原中的应用。这些内容体现了作者对前沿研究的系统把握，也展示了深度学习在复杂视觉问题中的强大潜力。

值得注意的是，作者并未停留在模型介绍层面，而是深入分析了不同网络在去噪、去模糊和超分辨率等任务中的适用性差异，为读者在实际研究和应用中选择合适方法提供了有力参考。

3、应用场景与实验验证

《深度学习与图像复原》在理论与方法之外，还通过大量实验验证展示了深度学习模型在真实场景中的表现。田春伟通过标准数据集与评价指标，系统比较了不同算法在复原效果上的差异。

书中列举了医学影像、遥感图像以及低照度图像等多个应用场景，说明图像复原技术在实际工程与科研中的广泛需求。这些案例不仅增强了内容的现实感，也凸显了研究成果的应用价值。

通过对实验结果的深入分析，作者揭示了模型性能提升背后的原因，并讨论了训练数据、网络深度以及计算资源等因素对最终效果的影响，使读者能够从实验中获得方法论层面的启示。

4、学术意义与发展前景

从学术角度看，《深度学习与图像复原》体现了跨学科融合的研究趋势。田春伟将深度学习理论与传统图像处理问题紧密结合，为相关领域提供了一种新的研究范式。

书中对当前研究挑战的总结同样具有启发意义，例如模型泛化能力、可解释性以及计算效率等问题。这些讨论引导读者思考未来研究可能的突破方向。

在发展前景方面，作者展望了图像复原技术与其他人工智能领域的融合潜力，强调该方向在智能感知、自动化系统以及数据分析中的重要作用，为后续研究提供了广阔想象空间。

总结：

总体而言，田春伟的《深度学习与图像复原》是一部兼具理论深度与实践价值的学术著作。它不仅系统梳理了图像复原领域的发展脉络，还通过引入深度学习方法，为传统问题注入了新的活力。

通过对理论基础、方法创新、应用实践和未来趋势的全面论述，本书为研究人员和工程实践者提供了清晰的研究思路与方法指导，在相关领域具有重要的参考价值。

本文由nayona.cn整理

点击联系需要东西方神秘学学习资料，专业的咨询

只要网页介绍资料，全部都有，还有很多还没来得及更新
每天更新200-300款资料
全网最大最全的神秘学资料平台
请需要什么资料，直接在对话框直接联系我，24小时在线，方便快捷
请需要什么资料，直接在对话框直接联系我，24小时在线，方便快捷
请需要什么资料，直接在对话框直接联系我，24小时在线，方便快捷

           

联系我们

关注公众号

打赏 微信扫一扫 支付宝扫一扫

雅书

对占星塔罗感兴趣关注公众号