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研究生:陳俊次
研究生(外文):Jun-CiChen
論文名稱:一個用於移除雨紋的增強式循環神經網路
論文名稱(外文):An Enhanced Recurrent Neural Network for Image Deraining
指導教授:戴顯權戴顯權引用關係
指導教授(外文):Shen-Chuan Tai
學位類別:碩士
校院名稱:國立成功大學
系所名稱:電腦與通信工程研究所
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:英文
論文頁數:51
中文關鍵詞:雨紋去除循環神經網路通道注意機制
外文關鍵詞:rain streak removalrecurrent neural networkchannel attention mechanism
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電腦視覺演算法像是物件偵測、影像辨識技術已經被導入許多日常生活的應用中,然而這些演算法通常沒有辦法在實際情形都運作的很完善,原因在於實際景象中,無法預測的影像品質損害情形時常會發生,例如:雜訊、曝光度,以及多變的氣候情況。常見的氣候情況像是下雨、下雪、沙塵暴都會嚴重地影響許多電腦視覺演算法的表現。
本論文提出一個基於循環神經網路的雨紋去除演算法來逐步地去除影像中的雨紋。一個預測雨紋的子網路用來預測一張有雨影像雨紋的部分,提供額外的輔助資訊來移除雨紋。一個結合通道注意機制的殘差密集模塊被應用在主要的循環神經網路來加強雨紋去除的能力。實驗結果顯示,本論文的方法在比較的方法當中,得到了更自然的紋理與細節。
Computer vision algorithms like object detection, image classification have changed our life significantly in various applications. However, these algorithms cannot usually have good performance in practical applications due to the fact that the unpredictable degradations often occur in realistic scene, for instance, noise, illumination, and severe weather conditions. Commonly seen weather conditions such as rain, snow, and sandstorm can adversely affect the performance of many computer vision tasks.
In this Thesis, a rain removal algorithm based on recurrent neural network is proposed to remove rain streak stage by stage. A Rain Streak Prediction Network is proposed to predict the rain streak part of a rainy image, providing more information to deraining. A residual dense block combining with channel attention mechanism, called RDCAB is used to enhance the ability of deraining. Experimental results show that the proposed method gets more nature textures and details compared with available methods.
Contents iv
List of Tables vi
List of Figures vii
Chapter 1 Introduction 1
Chapter 2 Background and Related Works 4
2.1 Rain Physical Properties & Rain Model 4
2.2 Related Work 6
2.3 Recurrent Neural Network 10
2.3.1 Simple Recurrent Neural Network 10
2.3.2 Long Short-Term Memory 11
2.3.3 Gate Recurrent Unit 13
Chapter 3 The Proposed Algorithm 15
3.1 Proposed Network Architecture 17
3.1.1 Rain Streak Prediction Network 18
3.1.2 Deraining Recurrent Neural Network 19
3.1.2.1 Convolutional Gated Recurrent Unit 20
3.1.2.2 Residual Dense Channel Attention Block 21
3.2 Loss Function 23
3.2.1 MSE Loss 23
3.2.2 SSIM Loss 24
3.2.3 Perceptual Loss 24
3.2.4 Total Loss Function 25
Chapter 4 Experimental Results 28
4.1 Experimental Dataset 28 
4.2 Parameter and Experimental Setting 31
4.3 Experimental Results of Simulated Images 32
4.4 Ablation Experimental Results 41
4.5 Application 44
Chapter 5 Conclusion and Future Work 46
5.1 Conclusion 46
5.2 Future Work 46
References 47
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