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研究生:莊佳龍
研究生(外文):Chuang, Chialung
論文名稱:分段直方圖等化之技術與研究
論文名稱(外文):Piece-Wise Histogram Equalization For Image Enhancement
指導教授:郭忠民郭忠民引用關係
指導教授(外文):Kuo, Chungming
口試委員:章定遠楊乃中郭忠民
口試委員(外文):Chang, TingyuanYang, NaichungKuo, Chungming
口試日期:2012-07-04
學位類別:碩士
校院名稱:義守大學
系所名稱:資訊工程學系碩士在職專班
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:2012
畢業學年度:100
語文別:中文
論文頁數:85
中文關鍵詞:直方圖等化分段單邊強化
外文關鍵詞:Histogram EqualizationPiece-WiseUnilateralism Enhanced
相關次數:
  • 被引用被引用:1
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  • 下載下載:22
  • 收藏至我的研究室書目清單書目收藏:0
近幾十年來,直方圖等化(HE)已有深入的研究,且HE在影像強化中是最普遍的技術,因為它沒有複雜的參數且能產生高效能的執行結果,直方圖等化被廣泛的使用在各種不同的影像處理上,例如和雷達訊號與醫學影像的處理過程,但直方圖等化往往會造成影像過度強化的現象,造成視覺品質大大的降低。
常見的直方圖等化方法是利用不同的分段機制,將原始影像分離為N張子圖,再對這些子圖進行個別強化,最後將分離的子圖合併,便可得到強化後的影像,但以目前的分段方法,無法保證影像視覺品質的提升,本文提出一個新的分段機制,利用單邊強化的方法,使強化後的影像能保持原始亮度,在主體與細節部分對比度也明顯提升,實驗結果證明,本文提出的方法,有效的提升影像的視覺品質,此外,我們將所提出的方法嘗試應於彩色影像上,並探討其實驗結果。
Histogram equalization (HE), which has been intensively studied for decades, is one of the most popular technologies because it can produce high performance results without complex parameters. Histogram equalization is widely used for a variety of image applications, for instance, radar signal processing and medical image processing. However, HE suffers from choosing a proper dynamic range, which could over-enhance images and causes poor visual quality.
Common HE methods use piece-wise algorithm that decomposes input image into N sub-images, and then enhances the sub-images individually. Result image is a combination of the enhanced sub-images. However, existing piece-wise algorithms do not guarantee successful enhancement. In this thesis, we propose a novel piece-wise algorithm that uses ‘’unilateralism’’ method to enhance the image details without loosing the original brightness of the source image. Results indicate the proposed method provides efficient enhancement. Furthermore, the proposed method is extended to enhance color images. Simulation results are demonstrated and discussed.
謝誌I
摘要II
Abstract III
目錄IV
圖目錄VI
表目錄VIII
第1章 緒論1
1.1 問題描述1
1.2 研究動機與背景2
1.3 論文架構3
第2章 相關研究回顧4
2.1 傳統直方圖等化4
2.2 傳統影像強化方法回顧8
2.2.1 保持亮度的雙直方圖等化法8
2.2.2 使用平均亮度最小誤差的雙直方圖等化法11
2.2.3 使用遞迴平均值分離來保持亮度的直方圖等化法15
2.2.4 使用動態範圍分離來增強影像對比的直方圖等化法19
第3章 單邊直方圖等化法25
3.1 影像與強化方法分析25
3.2 直方圖單邊區域強化27
3.2.1 以期望值為分段點來分離出子影像27
3.2.2 判定來源影像亮度與強化區域28
3.2.3 單邊區域強化結果與討論31
3.3 加入標準差與閥值來調整分段點33
3.4 單邊直方圖等化流程圖35
3.5 單邊直方圖等化嘗試應用於彩色影像上36
3.6 色彩空間36
3.6.1 RGB色彩空間37
3.6.2 YCbCr色彩空間37
3.7 彩色影像在YCbCr色彩空間中單邊直方圖等化39
3.8 單邊等化彩色影像流程圖40
第4章 實驗結果41
4.1 參數設定42
4.2 單邊直方圖等化之實驗結果42
4.3 單邊直方圖等化之調整系數的取捨60
4.4 單邊直方圖等化應用於彩色影像之實驗結果63
第5章 結論與未來發展69
參考文獻72
圖目錄
圖1-1、Couple原始影像1
圖2-1、直方圖等化示意圖4
圖2-2、HE強化Lena結果圖5
圖2-3、HE強化Couple結果圖6
圖2-4、HE強化Hands結果圖7
圖2-5、HE強化Hands直方圖7
圖2-6、HE強化F16結果圖7
圖2-7、BBHE強化Hands結果圖9
圖2-8、BBHE強化F16結果圖10
圖2-9、強化U2結果圖13
圖2-10、強化Copter結果圖14
圖2-11、RMSHE強化示意圖15
圖2-12、強化Girl結果圖18
圖2-13、計算WAAD示意圖19
圖2-14、計算WAAD前後直方圖20
圖2-15、灰階動態範圍偵測與分離20
圖2-16、灰階範圍重新對應示意圖22
圖2-17、強化elaine結果圖24
圖3-1、強化Putrajaya結果圖26
圖3-2、單邊強化示意圖30
圖3-3、單邊區域強化Aircraft結果圖32
圖3-4、單邊區域強化Copter結果圖32
圖3-5、單邊區域強化U2結果圖32
圖3-6、加法混色示意圖37
圖3-7、YCbCr色彩空間模型圖38
圖4-1、Aircraft實驗比較圖45
圖4-2、Putrajaya實驗比較圖47
圖4-3、F-16實驗比較圖49
圖4-4、House實驗比較圖52
圖4-5、Lena實驗比較圖54
圖4-6、U2實驗比較圖57
圖4-7、Hands實驗比較圖59
圖4-8、Aircraft調整系數取捨圖61
圖4-9、U2調整系數取捨圖61
圖4-10、Copter調整系數取捨圖62
圖4-11、Girl調整系數取捨圖62
圖4-12、彩色影像實驗一63
圖4-13、彩色影像實驗二64
圖4-14、彩色影像實驗三65
圖4-15、彩色影像實驗四66
圖4-16、彩色影像實驗五67
圖4-17、彩色影像實驗六68
表目錄
表2-1、AMBE與XT數據表12
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