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研究生:王詠霖
研究生(外文):WANG,YUNG-LIN
論文名稱:基於消除方塊效應之低動態範圍影像轉換為高動態範圍影像的殘值神經網路設計
論文名稱(外文):Residual Neural Network Design for Converting the Low Dynamic Range Image to the High Dynamic Range Image Based on Eliminating Block Effects
指導教授:蕭宇宏
指導教授(外文):SHIAU,YEU-HORNG
口試委員:陳培殷陳仁德
口試委員(外文):CHEN,PEI-YINCHEN,REN-DER
口試日期:2020-07-30
學位類別:碩士
校院名稱:國立雲林科技大學
系所名稱:電機工程系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:中文
論文頁數:73
中文關鍵詞:低動態範圍高動態範圍深度殘留神經網路模型方塊效應
外文關鍵詞:low dynamic rangehigh dynamic rangedeep residual neural network modelblock effects
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摘要 i
ABSTRACT ii
誌謝 iii
目錄 iv
表目錄 vi
圖目錄 vii
第一章 緒論 1
1.1研究背景與動機 1
1.2研究方向 3
1.3論文架構 5
第二章 相關演算法研究 6
2.1文獻探討 6
2.1.1 LDR轉換成HDR的研究 6
2.1.2單次拍攝高動態範圍成像基於深度卷積網路(Single-shot high dynamic range imaging via deep convolutional neural network) 12
2.1.3基於人類視覺系統之使用深度神經網路之反色調映射(Inverse Tone Mapping Operator Using Sequential Deep Neural Networks Based on the Human Visual System) 13
2.1.4低動態範圍影像轉換高動態範圍影像之深度殘留網路設計(Deep residual neural network design for converting the low dynamic range image to the high dynamic range image) 17
第三章 低動態範圍影像轉換高動態範圍殘留神經網路架構 20
3.1 卷積層區 20
3.1.1卷積神經網路(Convolutional Neural Network, CNN) 20
3.1.2殘留神經網路(Residual Neural Network, ResNet) 22
3.2 方塊效應(block effects) 23
3.4 Log轉換 24
3.5 激勵函數(Activate function) 24
3.6訓練方法與數據集 25
第四章 實驗結果 27
4.1使用色彩映射獲得的LDR 27
4.1.1使用色彩映射獲得之結果圖主觀比較 27
4.1.2使用色彩映射獲得之結果圖客觀比較 39
4.2使用多重曝光合成HDR影像取得其中間曝光值影像做為LDR 40
4.2.1使用HDR影像取得其中間曝光值影像之結果圖主觀比較 40
4.2.2使用HDR影像取得其中間曝光值影像之結果圖客觀比較 58
4.3 HDR-VDP-2.2 59
4.4 峰值信噪比(peak signal to noise ratio,PSNR) 60
4.5 均方根誤差(root-mean-square error,RMSE) 60
第五章 結論與未來工作 61
參考文獻 62

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