掃一掃
關注中圖網(wǎng)
官方微博
本類五星書更多>
-
>
決戰(zhàn)行測5000題(言語理解與表達)
-
>
軟件性能測試.分析與調優(yōu)實踐之路
-
>
第一行代碼Android
-
>
深度學習
-
>
Unreal Engine 4藍圖完全學習教程
-
>
深入理解計算機系統(tǒng)-原書第3版
-
>
Word/Excel PPT 2013辦公應用從入門到精通-(附贈1DVD.含語音視頻教學+辦公模板+PDF電子書)
深度學習與圖像復原 版權信息
- ISBN：9787121483042
- 條形碼：9787121483042 ; 978-7-121-48304-2
- 裝幀：平塑勒
- 冊數(shù)：暫無
- 重量：暫無
- 所屬分類：>
深度學習與圖像復原 內容簡介
隨著數(shù)字技術的飛速發(fā)展，圖像已成為一種至關重要的信息載體，無論是社交媒體上的圖像分享、新聞報道中的圖像應用，還是醫(yī)療領域的圖像分析，數(shù)字圖像都以其獨特的直觀性和高效性廣泛滲透于人們日常生活的諸多領域。然而，圖像質量往往受到相機晃動、噪聲干擾和光照不足等多種因素的影響，這給精確的圖像分析帶來了巨大挑戰(zhàn)。圖像復原技術可以消除受損圖像中的干擾信號，并重構高質量圖像。為此，本書深入剖析了圖像復原技術的*新進展，并探索了深度學習技術在圖像復原過程中的關鍵作用。本書集理論、技術、實踐于一體，不僅可以為相關領域的學者和學生提供寶貴的學術資源，還可以為工業(yè)界的專業(yè)人士提供利用先進技術解決實際問題的方法。本書面向對深度學習與圖像復原知識有興趣的愛好者及高校相關專業(yè)學生，期望讀者能有所收獲。
深度學習與圖像復原 目錄
第1 章 基于傳統(tǒng)機器學習的圖像復原方法 ............................................................. 11.1 圖像去噪 ···············································································11.1.1 圖像去噪任務簡介···························································11.1.2 基于傳統(tǒng)機器學習的圖像去噪方法 ·····································11.2 圖像超分辨率 ·········································································91.2.1 圖像超分辨率任務簡介 ····················································91.2.2 基于傳統(tǒng)機器學習的圖像超分辨率方法 ·······························91.3 圖像去水印 ·········································································.151.3.1 圖像去水印任務簡介 ····················································.151.3.2 基于傳統(tǒng)機器學習的圖像去水印方法 ·······························.151.4 本章小結 ············································································.19參考文獻 ···················································································.20第2 章 基于卷積神經網(wǎng)絡的圖像復原方法基礎 ................................................... 242.1 卷積層 ···············································································.24第1 章 基于傳統(tǒng)機器學習的圖像復原方法 ............................................................. 1
1.1 圖像去噪 ···············································································1
1.1.1 圖像去噪任務簡介···························································1
1.1.2 基于傳統(tǒng)機器學習的圖像去噪方法 ·····································1
1.2 圖像超分辨率 ·········································································9
1.2.1 圖像超分辨率任務簡介 ····················································9
1.2.2 基于傳統(tǒng)機器學習的圖像超分辨率方法 ·······························9
1.3 圖像去水印 ·········································································.15
1.3.1 圖像去水印任務簡介 ····················································.15
1.3.2 基于傳統(tǒng)機器學習的圖像去水印方法 ·······························.15
1.4 本章小結 ············································································.19
參考文獻 ···················································································.20
第2 章 基于卷積神經網(wǎng)絡的圖像復原方法基礎 ................................................... 24
2.1 卷積層 ···············································································.24
2.1.1 卷積操作 ····································································.26
2.1.2 感受野 ·······································································.29
2.1.3 多通道卷積和多卷積核卷積 ···········································.30
2.1.4 空洞卷積 ····································································.31
2.2 激活層 ···············································································.33
2.2.1 Sigmoid 激活函數(shù) ·························································.33
2.2.2 Softmax 激活函數(shù) ·························································.35
2.2.3 ReLU 激活函數(shù) ···························································.36
2.2.4 Leaky ReLU 激活函數(shù) ···················································.38
2.3 基于卷積神經網(wǎng)絡的圖像去噪方法 ···········································.39
2.3.1 研究背景 ····································································.39
2.3.2 網(wǎng)絡結構 ····································································.40
2.3.3 實驗結果 ····································································.42
2.3.4 研究意義 ····································································.47
2.4 基于卷積神經網(wǎng)絡的圖像超分辨率方法 ·····································.48
2.4.1 研究背景 ····································································.48
2.4.2 網(wǎng)絡結構 ····································································.48
2.4.3 實驗結果 ····································································.51
2.4.4 研究意義 ····································································.55
2.5 基于卷積神經網(wǎng)絡的圖像去水印方法 ········································.55
2.5.1 研究背景 ····································································.55
2.5.2 網(wǎng)絡結構 ····································································.56
2.5.3 實驗結果 ····································································.58
2.5.4 研究意義 ····································································.61
2.6 本章小結 ············································································.62
參考文獻 ···················································································.62
第3 章 基于雙路徑卷積神經網(wǎng)絡的圖像去噪方法 ............................................... 69
3.1 引言 ··················································································.69
3.2 相關技術 ············································································.70
3.2.1 空洞卷積技術 ······························································.70
3.2.2 殘差學習技術 ······························································.71
3.3 面向圖像去噪的雙路徑卷積神經網(wǎng)絡 ········································.72
3.3.1 網(wǎng)絡結構 ····································································.72
3.3.2 損失函數(shù) ····································································.74
3.3.3 重歸一化技術、空洞卷積技術和殘差學習技術的結合利用 ····.74
3.4 實驗結果與分析 ···································································.76
3.4.1 實驗設置 ····································································.77
3.4.2 關鍵技術的合理性和有效性驗證 ·····································.79
3.4.3 灰度與彩色高斯噪聲圖像去噪 ········································.83
3.4.4 真實噪聲圖像去噪························································.87
3.4.5 去噪網(wǎng)絡的復雜度及3
展開全部
深度學習與圖像復原 作者簡介
田春偉，西北工業(yè)大學副教授、博士生導師？仗斓睾Ｒ惑w化大數(shù)據(jù)應用技術國家工程實驗室成員。入選2023和2022年全球前2%頂尖科學家榜單、省級人才、市級人才、西北工業(yè)大學翱翔新星。研究方向為視頻/圖像復原和識別、圖像生成等。在國際期刊和國際會議上發(fā)表論文70余篇，其中6篇ESI高被引論文、3篇ESI熱點論文、4篇頂刊封面論文、5篇國際超分辨領域Benchmark List論文、3篇GitHub 2020具有貢獻代碼，1篇論文技術被美國醫(yī)學影像公司購買商用，1篇論文技術被日本工程師應用于蘋果手機上等。
書友推薦
- >
詩經-先民的歌唱
- >
二體千字文
- >
隨園食單
- >
山海經
- >
大紅狗在馬戲團-大紅狗克里弗-助人
- >
李白與唐代文化
- >
推拿
- >
姑媽的寶刀
本類暢銷
