臺灣博碩士論文加值系統

本研究針對高動態影像進行影像的視覺品質強化，由於過去針對許多影像的演算法當中，對於高動態影像動態範圍壓縮的分佈不盡完善，如階層直方圖增強法並無進行動態範圍的壓縮，只是將其階層圖等化，造成影像對比被過度增強，Gamma曲線也無法同時針對動態範圍極端分佈之高動態影像自動作出微調整，需手工加以微調整，Retinex增強法考量到人眼特性，卻會產生光暈現象，因此本研究以結合雙向濾波器及GDDRC演算法為基礎進行影像視覺強化的改良，利用雙向濾波器演算法優異的邊緣偵測與邊緣保留能力及避免光暈現象之優點並配合GDDRC演算法顯著的動態範圍壓縮能力加以運用，有效改善雙向濾波器動態範圍壓縮能力，使其能顯著的使高動態影像完整呈現。
主要因為雙向濾波器本身對於動態範圍壓縮之使用不是本身主要運用之優勢，因此本研究利用GDDRC顯著的動態範圍壓縮能力加以改良雙向濾波器之不足之處，並透過雙向濾波器可分離細節影像之效用，再加以結合GDDRC運用，可分離出更完整之細節影像，使最終結果影像之細節紋路能夠完整的呈現，使雙向濾波器與GDDRC演算法的結合運用上更加有效運用其優勢。

Mainly due to the Bilateral filter itself is the use of dynamic range compression is not in itself the main use of the advantages, so the present study, GDDRC significantly improved the ability to be Bilateral filter of the deficiencies and, through detailed images of the detachable Bilateral filter usefulness, combined with GDDRC to use, the details of the Detachable a more complete image, so that the final result image of the details of the lines can be a complete presentation, so that Bilateral filter and GDDRC algorithms on a combination of a more effective use of its advantages

摘要 2
圖目錄 5
第一章 �頧� 7
1.1 研究背景與動機 7
第二章、文獻探討 17
2.1 GAMMA 曲線增強法 – (GAMMA CURVE) 17
2.2 階層直方圖等化法 - HE (HISTOGRAM EQUALIZATION ) 18
2.3 S曲線增強法 – (S CURVE) 20
2.4 LCIS (LOW CURVATURE IMAGE SIMPLIFIER ) 21
2.5 雙向濾波器 (BILATERAL FILTER) 22
2.6　GDDRC (GRADIENT DOMAIN DYNAMIC RANGE COMPRESSION) 25
2.7 RETINEX 演算法 29
2.8 小結 29
第三章、研究方法 31
3.1 本研究提出之流程 32
3.1.1 A1流程-取完整細節流程： 34
3.1.2 A2流程-主要的動態範圍壓縮流程： 36
第四章、實驗結果與探討 39
4.1 實驗影像說明 39
4.2 實驗環境設備說明 40
4.3 演算法變數設定 41
4.4 三種增強法所呈現之結果影像比較 42
4.4.1 影像一實驗結果 42
4.4.2 影像二實驗結果 44
4.4.3 影像三實驗結果 46
4.4.4 影像實驗結果探討 48
4.5 本研究所提之增強法的優缺點 49
第五章、結論 56
參考文獻 57

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