深度学习与图像复原

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%顶尖科学家榜单、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。

内容简介

书籍目录

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3