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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 貝蘇章(Soo-Chang Pei) | |
dc.contributor.author | Yi-Kuei Liu | en |
dc.contributor.author | 劉怡逵 | zh_TW |
dc.date.accessioned | 2021-06-16T16:04:37Z | - |
dc.date.available | 2016-07-08 | |
dc.date.copyright | 2013-07-08 | |
dc.date.issued | 2013 | |
dc.date.submitted | 2013-06-25 | |
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Gunturk et.al., 'Color Plane Interpolation Using Alternating Projection ' IEEE transactions on image processing, VOL. 11, NO.9, September 2002 [33] Lei Zhang et.al., 'Color Demosaicking by Local Directional Interpolation and Nonlocal Adaptive Thresholding' J. Electron. Imaging. June 27, 2011. [34] R. Fattal. “Image upsampling via imposed edge statistics” In SIGGRAPH, 2007. [35] W. T. Freeman, T. R. Jones, and E. C. Pasztor. “Example-based super-resolution” Comp. Graph. Appl., (2), 2002. [36] K. Kim and Y. Kwon. “Example-based learning for single image SR and JPEG artifact removal” MPI-TR, (173), 08. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/62568 | - |
dc.description.abstract | 超解析度(Super-Resolution)一般泛指影像利用單張或是多張的低解析度影像透過重建、學習、補償以及後處理的方法來產生出具有豐富資訊及細節的高品質影像。高解析度的影像在日漸進步的高科技社會中越來越重要,舉凡醫學影像、工業影像、衛星影像甚至生活中監控系統影像及普通生活照都是極度被需求的。低解析度的影像容易造成模糊與方格效應(Lattice Effect),而超解析度(Super-Resolution)技術可以有效的改善這些問題。
在本論文中,我們提出一個基於重建色彩濾波矩陣(Color Filter Array)的超解析度影像演算法。相較於現有以多張重建或以學習為基礎的超解析度方法,我們的方法是非常有效率的。不僅具有銳利的邊界部分,亦擁有豐富的細節。實驗結果顯示本論文所提出的新方法對於增加影像解析度有令人滿意的效果。 | zh_TW |
dc.description.abstract | “Super-Resolution” generally refers to the images use a single frame or multiple frames of low resolution to produce high quality images with rich information and details.
The high-resolution images play more and more important roles in the high-tech community nowadays. Different kind of high resolution images are extremely demanded such as medical images, industrial images, and satellite images, surveillance camera images and even photos of a daily life. Low-resolution images may cause the “Lattice effect” and blur. However, super-resolution technology can improve these problems effectively. In this paper, we propose a super-resolution algorithm based on color filter array reconstruction. This algorithm only uses a single low-resolution image to scale up. Comparing to some proposed example-based super-resolution algorithms or some algorithm scale images up by multiple frames, our method is much more efficiently. The experiment and results show that the algorithm of this paper in increasing resolution of images has a satisfactory performance. | en |
dc.description.provenance | Made available in DSpace on 2021-06-16T16:04:37Z (GMT). No. of bitstreams: 1 ntu-102-R00942109-1.pdf: 21103971 bytes, checksum: f967fbba72de464f8822649581391dbf (MD5) Previous issue date: 2013 | en |
dc.description.tableofcontents | 口試委員會審定書……………………………………………………………………iii
誌謝……………………………………………………………………………………iv 中文摘要………………………………………………………………………………vi ABSTRACT…………………………………………………………………………viii CONTENTS……………………………………………………………………………x LIST OF FIGURES……………………………………………………………………xii LIST OF TABLES……………………………………………………………………xvi Chapter1 Introduction……………………………………………………………….1 Chapter2 Color Filter Array…………………………………………………………..3 2.1 The operation of digital camera………………………………………………4 2.2 Bayer pattern…………………………………………………………………5 Chapter 3 Related Method of Color Demosaicing……………………………….7 3.1 Nearest-neighbor replication…………………………………………………8 3.2 Bilinear Interpolation………………………………………………………10 3.3 Smooth hue transition interpolation…………………………………………12 3.4 Edge-sensing interpolation…………………….…………………………..14 3.5 Interpolation with Laplacian Second-Order Correction Terms…………….19 3.5.1 Interpolation with simple color correction terms ………………19 3.5.2 Interpolation with Laplacian second-order correction terms……...23 3.6 Threshold-based variable number of gradient interpolation…………………30 3.7 Pattern recognition interpolation………………. …………………………40 3.8 Local Direction Interpolation with Nonlocal Means filter…………………49 3.9 Alternating projection………………………………………………………59 3.10 Experimental Results………………………………………………………64 3.10.1 Result Image in Different Algorithms……………………………64 3.10.2 PSNR of Result Image……………………………………………65 Chapter 4 Related Method of Super Resolution………………………………66 4.1 Example-Based Super-Resolution…………………….……………………67 4.1.1 Markov Network Algorithm………………………………………69 4.1.2 The One-pass Algorithm…………………………………………71 4.2 Image Upsampling via Imposed Edge Statistics……………………………75 Chapter 5 Proposed Method of Super Resolution………………………………81 5.1 Super Resolution using direction Interpolation……………………………82 5.2 Proposed Method…………………………………..………………………85 5.2.1 Proposed Method 1………………………………………………85 5.2.2 Proposed Method 2………………………………………………91 5.3 Experimental Results……………………………….………………………98 Chapter 6 Conclusion and Future Work………………………………………106 6.1 Conclusion………………………………………….………………………106 6.2 Future Work……………………………………….………………………106 REFERENCE…………………………………….…………………………………107 | |
dc.language.iso | en | |
dc.title | 利用色彩濾波矩陣對單張圖片做超解析度圖像放大演算法 | zh_TW |
dc.title | Super-Resolution Algorithms using Color Filter Array from a Single Image | en |
dc.type | Thesis | |
dc.date.schoolyear | 101-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 鍾國亮(Kuo-Liang Chung),祈忠勇(Chong-Yung Chi),傅楸善(Chiou-Shann Fuh),林康平(Kang-Ping Lin) | |
dc.subject.keyword | 超解析度,色彩濾波矩陣,拜耳圖形,工業影像,交替投影,誤差補償, | zh_TW |
dc.subject.keyword | Super-resolution,Color filter array,Bayer pattern,Alternating projection,Error compensation, | en |
dc.relation.page | 110 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2013-06-26 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 電信工程學研究所 | zh_TW |
顯示於系所單位: | 電信工程學研究所 |
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