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
