描述
包 装: 平塑勒是否套装: 否国际标准书号ISBN: 9787121483042
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，这给精确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最新进展，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。
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
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
评论
还没有评论。