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深度學(xué)習(xí)
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深度學(xué)習(xí)與圖像復(fù)原 版權(quán)信息
- ISBN：9787121483042
- 條形碼：9787121483042 ; 978-7-121-48304-2
- 裝幀：平塑勒
- 冊(cè)數(shù)：暫無(wú)
- 重量：暫無(wú)
- 所屬分類：>
深度學(xué)習(xí)與圖像復(fù)原 內(nèi)容簡(jiǎn)介
隨著數(shù)字技術(shù)的飛速發(fā)展，圖像已成為一種至關(guān)重要的信息載體，無(wú)論是社交媒體上的圖像分享、新聞報(bào)道中的圖像應(yīng)用，還是醫(yī)療領(lǐng)域的圖像分析，數(shù)字圖像都以其獨(dú)特的直觀性和高效性廣泛滲透于人們?nèi)粘Ｉ畹闹T多領(lǐng)域。然而，圖像質(zhì)量往往受到相機(jī)晃動(dòng)、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(lái)了巨大挑戰(zhàn)。圖像復(fù)原技術(shù)可以消除受損圖像中的干擾信號(hào)，并重構(gòu)高質(zhì)量圖像。為此，本書(shū)深入剖析了圖像復(fù)原技術(shù)的*新進(jìn)展，并探索了深度學(xué)習(xí)技術(shù)在圖像復(fù)原過(guò)程中的關(guān)鍵作用。本書(shū)集理論、技術(shù)、實(shí)踐于一體，不僅可以為相關(guān)領(lǐng)域的學(xué)者和學(xué)生提供寶貴的學(xué)術(shù)資源，還可以為工業(yè)界的專業(yè)人士提供利用先進(jìn)技術(shù)解決實(shí)際問(wèn)題的方法。本書(shū)面向?qū)ι疃葘W(xué)習(xí)與圖像復(fù)原知識(shí)有興趣的愛(ài)好者及高校相關(guān)專業(yè)學(xué)生，期望讀者能有所收獲。
深度學(xué)習(xí)與圖像復(fù)原 目錄
第1 章 基于傳統(tǒng)機(jī)器學(xué)習(xí)的圖像復(fù)原方法 ............................................................. 11.1 圖像去噪 ···············································································11.1.1 圖像去噪任務(wù)簡(jiǎn)介···························································11.1.2 基于傳統(tǒng)機(jī)器學(xué)習(xí)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(wù)簡(jiǎn)介 ····················································91.2.2 基于傳統(tǒng)機(jī)器學(xué)習(xí)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(wù)簡(jiǎn)介 ····················································.151.3.2 基于傳統(tǒng)機(jī)器學(xué)習(xí)的圖像去水印方法 ·······························.151.4 本章小結(jié) ············································································.19參考文獻(xiàn) ···················································································.20第2 章 基于卷積神經(jīng)網(wǎng)絡(luò)的圖像復(fù)原方法基礎(chǔ) ................................................... 242.1 卷積層 ···············································································.24第1 章 基于傳統(tǒng)機(jī)器學(xué)習(xí)的圖像復(fù)原方法 ............................................................. 1
1.1 圖像去噪 ···············································································1
1.1.1 圖像去噪任務(wù)簡(jiǎn)介···························································1
1.1.2 基于傳統(tǒng)機(jī)器學(xué)習(xí)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(wù)簡(jiǎn)介 ····················································9
1.2.2 基于傳統(tǒng)機(jī)器學(xué)習(xí)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(wù)簡(jiǎn)介 ····················································.15
1.3.2 基于傳統(tǒng)機(jī)器學(xué)習(xí)的圖像去水印方法 ·······························.15
1.4 本章小結(jié) ············································································.19
參考文獻(xiàn) ···················································································.20
第2 章 基于卷積神經(jīng)網(wǎng)絡(luò)的圖像復(fù)原方法基礎(chǔ) ................................................... 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(jīng)網(wǎng)絡(luò)的圖像去噪方法 ···········································.39
2.3.1 研究背景 ····································································.39
2.3.2 網(wǎng)絡(luò)結(jié)構(gòu) ····································································.40
2.3.3 實(shí)驗(yàn)結(jié)果 ····································································.42
2.3.4 研究意義 ····································································.47
2.4 基于卷積神經(jīng)網(wǎng)絡(luò)的圖像超分辨率方法 ·····································.48
2.4.1 研究背景 ····································································.48
2.4.2 網(wǎng)絡(luò)結(jié)構(gòu) ····································································.48
2.4.3 實(shí)驗(yàn)結(jié)果 ····································································.51
2.4.4 研究意義 ····································································.55
2.5 基于卷積神經(jīng)網(wǎng)絡(luò)的圖像去水印方法 ········································.55
2.5.1 研究背景 ····································································.55
2.5.2 網(wǎng)絡(luò)結(jié)構(gòu) ····································································.56
2.5.3 實(shí)驗(yàn)結(jié)果 ····································································.58
2.5.4 研究意義 ····································································.61
2.6 本章小結(jié) ············································································.62
參考文獻(xiàn) ···················································································.62
第3 章 基于雙路徑卷積神經(jīng)網(wǎng)絡(luò)的圖像去噪方法 ............................................... 69
3.1 引言 ··················································································.69
3.2 相關(guān)技術(shù) ············································································.70
3.2.1 空洞卷積技術(shù) ······························································.70
3.2.2 殘差學(xué)習(xí)技術(shù) ······························································.71
3.3 面向圖像去噪的雙路徑卷積神經(jīng)網(wǎng)絡(luò) ········································.72
3.3.1 網(wǎng)絡(luò)結(jié)構(gòu) ····································································.72
3.3.2 損失函數(shù) ····································································.74
3.3.3 重歸一化技術(shù)、空洞卷積技術(shù)和殘差學(xué)習(xí)技術(shù)的結(jié)合利用 ····.74
3.4 實(shí)驗(yàn)結(jié)果與分析 ···································································.76
3.4.1 實(shí)驗(yàn)設(shè)置 ····································································.77
3.4.2 關(guān)鍵技術(shù)的合理性和有效性驗(yàn)證 ·····································.79
3.4.3 灰度與彩色高斯噪聲圖像去噪 ········································.83
3.4.4 真實(shí)噪聲圖像去噪························································.87
3.4.5 去噪網(wǎng)絡(luò)的復(fù)雜度及3
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深度學(xué)習(xí)與圖像復(fù)原 作者簡(jiǎn)介
田春偉，西北工業(yè)大學(xué)副教授、博士生導(dǎo)師。空天地海一體化大數(shù)據(jù)應(yīng)用技術(shù)國(guó)家工程實(shí)驗(yàn)室成員。入選2023和2022年全球前2%頂尖科學(xué)家榜單、省級(jí)人才、市級(jí)人才、西北工業(yè)大學(xué)翱翔新星。研究方向?yàn)橐曨l/圖像復(fù)原和識(shí)別、圖像生成等。在國(guó)際期刊和國(guó)際會(huì)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(diǎn)論文、4篇頂刊封面論文、5篇國(guó)際超分辨領(lǐng)域Benchmark List論文、3篇GitHub 2020具有貢獻(xiàn)代碼，1篇論文技術(shù)被美國(guó)醫(yī)學(xué)影像公司購(gòu)買商用，1篇論文技術(shù)被日本工程師應(yīng)用于蘋果手機(jī)上等。
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