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研究生:林徽瀞
研究生(外文):Hui-Jing Lin
論文名稱:以明暗度校正及細節加強作多重曝光影像融合
論文名稱(外文):Multiexposure Image Fusion Using Intensity Correction and Detail Enhancement
指導教授:柳金章柳金章引用關係
指導教授(外文):Jin-Jang Leou
口試委員:廖弘源沈岱範康立威柳金章
口試委員(外文): Jin-Jang Leou
口試日期:2014-07-31
學位類別:碩士
校院名稱:國立中正大學
系所名稱:資訊工程研究所
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:2014
畢業學年度:102
語文別:英文
論文頁數:91
中文關鍵詞:高動態範圍影像低動態範圍影像多重曝光影像融合細節加強
外文關鍵詞:high dynamic range imagelow dynamic range imagemultiexposure image fusiondetail enhancement
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多重曝光影像融合技術是由多張不同曝光度的低動態範圍影像藉由計算出的權重直接合成出一張擁有較多場景細節的似高動態範圍影像而不用真正轉換至高動態範圍域的影像處理技術。在本論文中,提出一種使用明暗度校正以及細節強化的多重曝光影像融合方法。本論文所提出的方法包含有九個步驟,首先,利用對比限制適應性直方圖等化及homomorphic濾波來校正明暗度,並以校正後的明暗度使用跨影像中值濾波產生參考明暗度影像,然後使用gamma校正來補償飽和度遺失。之後,利用weighted least square (WLS)最佳化以及L0 smoothing濾波器以參考影像進行just-noticeable distortion (JND)估測得到的參數擷取出細節。利用可視性以及跨影像的一致性計算出相對權重,並利用cross bilateral濾波進行精準化。最後,以multiresolution spline based scheme取得似高動態範圍的結果影像。根據本研究的實驗結果顯示,本論文所提出的研究方法優於比較的四種現有方法。
Based on multiple low dynamic range (LDR) images with different exposures, multiexposure image fusion computes weighting maps of the LDR images to generate a high dynamic range like (HDR-like) image. In this study, a multiexposure image fusion approach is proposed. Within the proposed approach, contrast limited adaptive histogram equalization (CLAHE) and homomorphic filtering are used to correct the intensity of each LDR image, cross-image median filtering is used to generate reference image, and gamma correction is used to compensate saturation loss. Then, weighted least square (WLS) optimization and the L0 smoothing filter are used to extract details by parameter which estimated by just noticeable distortion (JND) with reference image. Weighting maps for each LDR image are computed by visibility and cross-image consistency and refined by cross bilateral filter. Finally, an HDR-like image is achieved by multiresolution spline based scheme. Base on the experimental results obtained in this study, the performance of the proposed approach is better than those of four comparison approaches.
摘 要 i
ABSTRACT iii
ACKNOWLEDGEMENTS iv
TABLE OF CONTENTS v
LIST OF FIGURES vii
LIST OF TABLES xiv
CHAPTER 1 INTRODUCTION 1
1.1. Motivation 1
1.2. Survey of Related Researches 2
1.3. Overview of Proposed Approach 8
1.4. Thesis Organization 9
CHAPTER 2 PROPOSED MULTIEXPOSURE IMAGE FUSION APPROACH FOR STATIC SCENES 10
2.1. System Architecture 10
2.2. Intensity Correction 11
2.3. Reference Intensity Image Composition 13
2.4. Just Noticeable Distortion (JND) Estimation 16
2.5. Gamma Correction 16
2.6. Detail Extraction 17
2.6.1. Local detail extraction 18
2.6.2. Global detail extraction 20
2.7. Weighting Map Estimation and Refinement 22
2.7.1. Weighting map estimation of visibility 23
2.7.2. Weighting map estimation of cross-image consistency 25
2.7.3. Weighting map refinement 27
2.8. Image Fusion 28
CHAPTER 3 EXPERIMENTAL RESULTS 29
3.1. System Setup 29
3.2. Image Quality Measures 29
3.3. Computational Complexity 64
CHAPTER 4 CONCLUSIONS 67
REFERENCES 68

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