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Jingdong book

图像超分辨率技术及其应用

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Jingdong book

图像超分辨率技术及其应用

Name: 图像超分辨率技术及其应用
Brand: Jingdong book
SKU: 3118027011
Price: 54.24 USD
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Content Description

《图像超分辨率技术及其应用》主要论述了图像超分辨率技术，即从单幅或多幅低分辨率图像获取高分辨率图像。尽管已有的图像插值和超分辨技术的算法众多．但仍需对此两种技术建立统一联系。《图像超分辨率技术及其应用》一书正是为实现此目的而出版，将图像插值作为图像超分辨率重建过程中的一个处理模块对待。
《图像超分辨率技术及其应用》并不是将图像插僮作为基于多项式问题或逆问题对待，而是对上述两种技术进行比较分析。同时，书中论述了图像超分辨率技术的两个方向：有先验知识的超分辨重建和盲超分辨率图像重建技术。另外，书中也对图像超分辨率技术的两个补充步骤——图像配准和图像融合——进行了详细讨论。
对彩色图像插值和插值技术在模式识别中的应用技术进行了详细讨论。
对基于逆问题的图像插值技术进行了分析。
介绍了图像配准方法。
讨论了图像融合及其在图像超分辨率技术中的应用。
给出了仿真试验结果及相应的MATLAB？码。
《图像超分辨率技术及其应用》涵盖了图像超分辨率技术及其应用，并介绍了医学和卫星图像超分辨率技术的具体应用。用MATLAB编程实现了包括多项式图像插值和自适应多项式图像插值技术在内的多种图像超分辨率技术。书中所讨论的仿真代码均在附录中给出。

Author Description

Fathi E．Abd El-Samie，分别于1998年、2001年、2005年于埃及Menoufia大学获得学士、硕士、博士学位。自2005年在电子通信学院任教，至今在国际会议及学术期刊发表学术论文160余篇。当前研究领域为图像增强、复原、插值，超分辨图像重建，数据加密，多媒体通信技术，医学图像和光学信号处理以及数字通信等。Abd El-Samie博士在2008年获得“数字信号处理”期刊的被引用最多论文奖。

Mohiy M．Hadhoud，分别于1976年、1981年于埃及Menoufia大学获得学士、硕士学位，于1987年于Southampton大学获得博士学位。1981-2001年于埃及Menoufia大学电子通信学院任教，目前为埃及Menoufia大学信息技术学院教授。
Mohiy M．Hadhoud博士已经在国内外学术期刊和会议上发表学术论文100余篇，当前研究领域为自适应信号及图像处理，图像增强、复原，超分辨图像重建，数据加密及图像色彩处理等。

Said E．E1．Khamy，1971年于马萨诸塞大学阿默斯特分校（university of Massachusetts，Amherst）获得博士学位，目前为埃及Alexandria大学电子工程学院教授。2000年9月-2003年9月任该学院院长。鉴于其学术水平，他在沙特阿拉伯、伊朗、黎巴嫩以及阿拉伯科技学院（AAST）任教。Said E．El-Khamy博士当前研究领域为移动通信，不同介质中的波传播，智能天线阵列，图像处理与水印，以及现代信号处理技术（神经网络、小波分析、遗传算法、分形、HOS与模糊算法等）及其在图像处理、通信系统、天线设计和波传播中的应用。至今为止已在国内外学术期刊和会议上发表学术论文300余篇。

Catalogue

第1章绪论
1.1 图像插值
1.2 图像超分辨率重建

第2章多项式图像插值
2.1 引言
2.2 经典图像插值算法
2.3 B样条图像插值
2.3.1 多项式样条
2.3.2 B样条类型
2.3.3 B样条插值的数字滤波器实
2.4 O-MOMS插值法
2.5 Keys'（双三次）插值
2.6 多项式图像插值的问题
2.6.1 振铃效应
2.6.2 混叠效应
2.6.3 块效应
2.6.4 模糊效应

第3章自适应多项式图像插值
3.1 引言
3.2 低分辨率图像降质模型
3.3 线性不变空间图像插值
3.4 有偏距离图像插值
3.5 权重图像插值
3.6 迭代图像插值
3.7 仿真实例

第4章基于神经网络的多项式图像插值
4.1 引言
4.2 ANN基础知识
4.2.1 神经元
4.2.2 层
4.2.3 连接
4.2.4 权重
4.2.5 激励规则
4.2.6 激励函数
4.2.7 输出
4.2.8 学习规则
4.3 神经网络结构
4.3.1 多层感知网络
4.3.2 径向基函数网络
4.3.3 小波神经网络
4.3.4 循环神经网络
4.4 训练算法
4.5 神经网络图像插值
4.6 仿真试验

第5章彩色图像插值
5.1 引言
5.2 色彩过滤阵列
5.2.1 白平衡
5.2.2 Beyer插值
5.3 拉普拉斯二阶矫正的线性插值
5.4 自适应彩色图像插值

第6章图像插值在模式识别中的应用
6.1 引言
6.2 倒频谱模式识别技术
6.3 特征提取
6.3.1 MFCC系数计算
6.3.2 多项式系数
6.4 基于离散变换的特征提取
6.4.1 离散小波变换
6.4.2 离散余弦变换
6.4.3 离散正弦变换
6.5 基于人工神经网络的特征匹配
6.6 仿真实例

第7章图像插值逆问题
7.1 引言
7.2 自适应最小二乘图像插值
7.3 LMMSE图像插值
7.4 最大熵图像插值
7.5 正则化图像插值
7.6 仿真试验
7.7 红外图像的插值

第8章图像配准
8.1 引言
8.2 图像配准的应用
8.2.1 不同视角（多视角分析）
8.2.2 不同时间（多时相分析）
8.2.3 不同传感器（多模型分析）
8.2.4 场景和模型的配准
8.3 图像配准的步骤
8.3.1 特征检测步骤
8.3.2 特征匹配步骤
8.3.3 变换模型估计
8.3.4 图像重采样和变换
8.4 图像配准精度的估计

第9章图像融合
9.1 引言
9.2 图像融合的目标
9.3 图像融合的实现
9.4 像素级图像融合
9.5 主元分析融合
9.6 小波融合
9.6.1 DWT融合
9.6.2 DWFT融合
9.7 曲波融合
9.7.1 子带滤波
9.7.2 分割
9.7.3 脊波变换
9.8 IHS融合
9.9 高通滤波融合
9.10 格兰施密特融合
9.11 卫星图像融合
9.12 MR和CT图像融合

第10章基于先验知识的超分辨率重建
10.1 引言
10.2 多观测LR降质模型
10.3 基于小波的图像超分辨率重建
10.4 简化的多通道降质模型
10.5 多通道图像复原
10.5.1 多通道LMMSE复原
10.5.2 多通道最大熵复原
10.5.3 多通道正则化复原
10.6 仿真实例

第11章无先验知识的超分辨率重建
11.1 引言
11.2 问题描述
11.3 二维GCD算法
11.4 盲超分辨率重建方法
11.5 仿真试验

附录A 离散B样条
附录B 托普利茨一循环近似
附录C 牛顿算法
附录D MATLAB？代码
参考文献
索引

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