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題名 透過最小-最大均值池化濾波器與多頭自注意力卷積神經網路之影像雜訊移除研究
Image Denoising Using Min-Max Mean Pooling Filters and Multi-Head Self-Attention Convolutional Neural Networks作者 陸妍諭
Lu, Yen-Yu貢獻者 張宏慶
Jang, Hung-Chin
陸妍諭
Lu, Yen-Yu關鍵詞 多頭自注意力神經網路
卷積神經網路
深度學習
均值濾波器
椒鹽雜訊
池化濾波
multi-head self-attention neural network
convolutional neural network
deep learning
mean filter
salt-and-pepper noise
pooling-based filtering日期 2025 上傳時間 1-Jul-2025 15:06:44 (UTC+8) 摘要 數位影像在傳輸的過程中，可能會受到電磁干擾和攝影元件受損，導致影像受到脈衝雜訊干擾而損壞，如何有效修復遭受極值脈衝雜訊(椒鹽雜訊)干擾的影像，對於提升數位影像品質極為重要。本文提出一種使用自適應分析視窗最小-最大均值池化的多頭自注意力卷積神經網路，去除在傳輸過程中產生的椒鹽雜訊；首先，估測影像的雜訊密度，若為輕度雜訊干擾之影像，乾淨像素足夠，使用多頭自注意力卷積神經網路，計算輸入序列中不同位置像素的權重，並擷取長距離像素間的相關性，預測適合重建的臨域像素；相對的，若是中、高雜訊密度干擾時，受損像素較多，則透過多層的最小值和最大值均值池化濾波器，分別計算最大池化後的影像和最小池化後的兩類影像；最後將處理後的影像重新組合，並進行均值濾波處理；在高雜訊密度的環境中，若分析視窗沒有乾淨像素可以參考，則擴大分析視窗的尺寸，增加鄰域未受干擾像素引入的機率，修復影像中的受損像素。經由實驗結果證明：本文所提出的濾波器可以在各種雜訊密度有效重建受到雜訊干擾的影像，重建效果也優於許多極為先進(state-of-the-art)的演算法。
During the transmission of digital images, electromagnetic interference and sensor defects can result in impulse noise corruption, leading to severe image degradation. Effectively restoring images contaminated by extreme impulse noise, such as salt-and-pepper noise, is crucial for improving digital image quality. This paper proposes a multi-head self-attention neural network enhanced by adaptive min-max mean pooling to remove salt-and-pepper noise introduced during transmission. First, the noise density of the corrupted image is estimated. Suppose the image is subjected to low-level noise and contains a sufficient number of clean pixels; a multi-head self-attention mechanism is employed to compute the attention weights of pixels at different positions in the input sequence. This mechanism captures long-range dependencies to predict suitable neighborhood pixels for reconstruction. In contrast, under moderate to high noise density, where a larger portion of pixels are corrupted, the method utilizes multiple layers of min and max mean pooling filters to compute two feature maps separately based on maximum and minimum pooling operations. The processed images are then fused and further refined using mean filtering. In high-noise scenarios where no clean pixels are available within a small analysis window, the window size is adaptively enlarged to increase the likelihood of including undisturbed neighboring pixels, thereby improving the reconstruction of damaged regions. Experimental results demonstrate that the proposed filtering method can effectively restore noise-corrupted images across various noise levels, outperforming several state-of-the-art algorithms regarding reconstruction quality.參考文獻 [1] R. C. Gonzalez, R. E. Woods, and S. L. Eddins, Digital Image Processing, 2nd ed. New York, NY, USA: Prentice-Hall, 2002. [2] J. D. Gibson and A. 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國立政治大學
資訊科學系
112753205資料來源 http://thesis.lib.nccu.edu.tw/record/#G0112753205 資料類型 thesis dc.contributor.advisor 張宏慶 zh_TW dc.contributor.advisor Jang, Hung-Chin en_US dc.contributor.author (Authors) 陸妍諭 zh_TW dc.contributor.author (Authors) Lu, Yen-Yu en_US dc.creator (作者) 陸妍諭 zh_TW dc.creator (作者) Lu, Yen-Yu en_US dc.date (日期) 2025 en_US dc.date.accessioned 1-Jul-2025 15:06:44 (UTC+8) - dc.date.available 1-Jul-2025 15:06:44 (UTC+8) - dc.date.issued (上傳時間) 1-Jul-2025 15:06:44 (UTC+8) - dc.identifier (Other Identifiers) G0112753205 en_US dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/157813 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊科學系 zh_TW dc.description (描述) 112753205 zh_TW dc.description.abstract (摘要) 數位影像在傳輸的過程中，可能會受到電磁干擾和攝影元件受損，導致影像受到脈衝雜訊干擾而損壞，如何有效修復遭受極值脈衝雜訊(椒鹽雜訊)干擾的影像，對於提升數位影像品質極為重要。本文提出一種使用自適應分析視窗最小-最大均值池化的多頭自注意力卷積神經網路，去除在傳輸過程中產生的椒鹽雜訊；首先，估測影像的雜訊密度，若為輕度雜訊干擾之影像，乾淨像素足夠，使用多頭自注意力卷積神經網路，計算輸入序列中不同位置像素的權重，並擷取長距離像素間的相關性，預測適合重建的臨域像素；相對的，若是中、高雜訊密度干擾時，受損像素較多，則透過多層的最小值和最大值均值池化濾波器，分別計算最大池化後的影像和最小池化後的兩類影像；最後將處理後的影像重新組合，並進行均值濾波處理；在高雜訊密度的環境中，若分析視窗沒有乾淨像素可以參考，則擴大分析視窗的尺寸，增加鄰域未受干擾像素引入的機率，修復影像中的受損像素。經由實驗結果證明：本文所提出的濾波器可以在各種雜訊密度有效重建受到雜訊干擾的影像，重建效果也優於許多極為先進(state-of-the-art)的演算法。 zh_TW dc.description.abstract (摘要) During the transmission of digital images, electromagnetic interference and sensor defects can result in impulse noise corruption, leading to severe image degradation. Effectively restoring images contaminated by extreme impulse noise, such as salt-and-pepper noise, is crucial for improving digital image quality. This paper proposes a multi-head self-attention neural network enhanced by adaptive min-max mean pooling to remove salt-and-pepper noise introduced during transmission. First, the noise density of the corrupted image is estimated. Suppose the image is subjected to low-level noise and contains a sufficient number of clean pixels; a multi-head self-attention mechanism is employed to compute the attention weights of pixels at different positions in the input sequence. This mechanism captures long-range dependencies to predict suitable neighborhood pixels for reconstruction. In contrast, under moderate to high noise density, where a larger portion of pixels are corrupted, the method utilizes multiple layers of min and max mean pooling filters to compute two feature maps separately based on maximum and minimum pooling operations. The processed images are then fused and further refined using mean filtering. In high-noise scenarios where no clean pixels are available within a small analysis window, the window size is adaptively enlarged to increase the likelihood of including undisturbed neighboring pixels, thereby improving the reconstruction of damaged regions. Experimental results demonstrate that the proposed filtering method can effectively restore noise-corrupted images across various noise levels, outperforming several state-of-the-art algorithms regarding reconstruction quality. en_US dc.description.tableofcontents 謝辭 I 摘要 II ABSTRACT III 目錄 IV 圖目錄 VI 表目錄 IX 第一章 緒論 1 1.1 研究動機與目的 1 1.2 文獻探討 1 1.2.1 傳統濾波方法的改進與應用 1 1.2.2選擇性濾波技術的創新研究 3 1.2.3深度學習技術在影像修復中的應用 4 1.2.4深度學習技術在醫學影像中的應用 5 1.3章節結構 6 第二章 數位影像的雜訊移除方法 7 2.1數位影像表示法 7 2.2常見的影像雜訊 8 2.2.1脈衝雜訊 8 2.2.2高斯雜訊 9 2.2.3均勻雜訊 9 2.2.4雷萊雜訊 10 2.3灰階影像的雜訊生成 11 2.4檢測影像中的極值像素與鏡射方法 11 2.4.1偵測雜訊像素 12 2.4.2鏡射 13 2.5數位影像雜訊濾波演算法 13 2.5.1中值濾波器 14 2.5.2均值濾波器 14 2.5.3切換式自適性權重均值濾波器 15 2.5.4脈衝雜訊偵測用於開關中值濾波器 16 2.5.5中間值權重中位數濾波器 17 2.5.6方向權重中值濾波器 18 2.5.7修正型之方向權重中值濾波 19 2.5.8修正型非對稱剪枝中值濾波器 21 2.5.9可變視窗與像素機率調適之方法 22 2.6深度學習於影像雜訊濾波演算法 24 2.6.1卷積自動編碼器去雜訊中值濾波器 24 2.6.2使用卷積神經網路進行影像雜訊移除方法 25 2.6.3使用DLNN辨識像素變化方向的雜訊移除方法 26 2.6.4使用選擇性卷積塊之雜訊移除方法 27 2.6.5使用深度學習全連接神經網路均值濾波器進行影像增強 28 第三章 結合最小-最大均值池化濾波與多頭自注意力卷積神經網路之影像重建系統 29 3.1 不同雜訊密度的影像修復演算法 31 3.2 卷積神經網路介紹 32 3.3 多頭自注意力卷積神經網路的訓練影像蒐集方法 34 3.4 多頭自注意力卷積神經網路的雜訊移除方法 41 3.5 多頭自注意力卷積神經網路的設計架構 42 3.6 多頭自注意力卷積神經網路修復受損像素 44 3.7 最小-最大均值池化濾波 47 3.8 自適應分析視窗最小-最大均值池化與多頭自注意力卷積神經網路濾波器 50 第四章 實驗結果 51 4.1 實驗環境 51 4.2 PSNR效能評估 52 4.3 MSSIM效能評估 59 4.4 BOAT影像遭受固定式脈衝雜訊干擾之實驗結果 68 4.5 BABOON影像遭受固定式脈衝雜訊干擾之實驗結果 80 4.6 CAMERAMAN影像遭受固定式脈衝雜訊干擾之實驗結果 92 4.7 LAKE影像遭受固定式脈衝雜訊干擾之實驗結果 104 第五章 結論與未來研究方向 116 5.1 結論 116 5.2 未來研究方向 116 參考文獻 118 附錄一 符號對照表 125 附錄二 論文發表列表 128 zh_TW dc.format.extent 12720780 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0112753205 en_US dc.subject (關鍵詞) 多頭自注意力神經網路 zh_TW dc.subject (關鍵詞) 卷積神經網路 zh_TW dc.subject (關鍵詞) 深度學習 zh_TW dc.subject (關鍵詞) 均值濾波器 zh_TW dc.subject (關鍵詞) 椒鹽雜訊 zh_TW dc.subject (關鍵詞) 池化濾波 zh_TW dc.subject (關鍵詞) multi-head self-attention neural network en_US dc.subject (關鍵詞) convolutional neural network en_US dc.subject (關鍵詞) deep learning en_US dc.subject (關鍵詞) mean filter en_US dc.subject (關鍵詞) salt-and-pepper noise en_US dc.subject (關鍵詞) pooling-based filtering en_US dc.title (題名) 透過最小-最大均值池化濾波器與多頭自注意力卷積神經網路之影像雜訊移除研究 zh_TW dc.title (題名) Image Denoising Using Min-Max Mean Pooling Filters and Multi-Head Self-Attention Convolutional Neural Networks en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) [1] R. 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