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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 張恆華 | |
| dc.contributor.author | Cheng-Ho Wu | en |
| dc.contributor.author | 吳振和 | zh_TW |
| dc.date.accessioned | 2021-06-16T17:44:04Z | - |
| dc.date.available | 2019-08-13 | |
| dc.date.copyright | 2012-08-17 | |
| dc.date.issued | 2012 | |
| dc.date.submitted | 2012-08-14 | |
| dc.identifier.citation | [1] I. Pitas, Digital Image Processing Algorithms and Applications: Wiley, 2000.
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/64383 | - |
| dc.description.abstract | 數位影像在擷取傳輸的過程中，會因為各種因素的干擾而產生雜訊，而有些因素則是無法避免的。這些影像中的雜訊，除了對影像視覺上的品質產生影響外，也會降低影像處理的精確度。因此許多影像處理的演算法，在處理前都會對影像做去雜訊的處理。其中，多數的去雜訊演算法會使用雜訊資訊，作為處理過程中的參數。除此之外，在影像處理及電腦視覺的領域中，許多演算法都會使用到雜訊程度的資訊。現今，大部分影像雜訊估計的方法，對於低雜訊的影像會有過度估計的現象。本論文之目標為提出一個新的方法以提升低雜訊影像估計的精確度。我們假設雜訊的模型為高斯雜訊，所提出的方法則是以其統計特性作為方法設計的基礎。首先，使用正規化剪切方法將影像的像素群聚成超級像素。接著，透過這些超級像素內的統計特性，判斷其是否與雜訊相似。從這些超級像素中找出具代表性的區塊，並以其資訊計算估計結果。我們使用不同類型的數位影像及柏克萊影像資料庫進行實驗，實驗結果顯示本研究所提出的雜訊估計方法，在低雜訊的影像能有更精確的估計結果，在雜訊增加時也能保有一定的精確準度。 | zh_TW |
| dc.description.abstract | Noise estimation is essential in a wide variety of digital image processing applications. It provides an adaptive mechanism for many restoration algorithms instead of using fixed values for the amount of noise. In this thesis, we propose a new statistical method based on the superpixel maps for estimating the variance of additive Gaussian noise in images. The proposed approach consists of three major phases: superpixel classification, local variance computation, and statistical determination. Experimental results suggest that the proposed method provides good estimation and is of potential in many image restoration applications that require automation. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T17:44:04Z (GMT). No. of bitstreams: 1 ntu-101-R99525047-1.pdf: 3512851 bytes, checksum: 3ea81f9052107df1c80f33d15adc1bb6 (MD5) Previous issue date: 2012 | en |
| dc.description.tableofcontents | 致謝 i
中文摘要 ii ABSTRACT iii 目錄 iv 圖目錄 vi 表目錄 viii 第 1 章 緒論 1 1.1 研究背景與動機 1 1.2 研究目的 2 1.3 論文架構 3 第 2 章 文獻探討 4 2.1 影像模型 4 2.1.1 二元影像(Binary image) 5 2.1.2 灰階影像(Gray level image) 5 2.1.3 RGB 色彩影像模組 (RGB color model) 6 2.1.4 數位影像退化模組 7 2.2 影像雜訊 7 2.2.1 高斯雜訊(Gaussian noise) 8 2.2.2 雷利分佈雜訊(Rayleigh noise) 9 2.2.3 脈衝雜訊(Impulse noise) 10 2.3 雜訊估計 11 2.3.1 基於平滑處理之估計方法 11 2.3.2 基於拉普拉斯運算之估計方法 12 2.3.3 基於拉普拉斯轉換之估計方法改良 14 2.3.4 統計式的估計方法 16 第 3 章 研究設計 18 3.1 高斯雜訊之特性 18 3.2 方法流程 20 3.2.1 超級像素分類(Superpixel classification) 21 3.2.1.1 正規化剪切 21 3.2.2 區域變異數計算(Local variance computation) 26 3.2.3 統計分析判定(Statistical determination) 27 第 4 章 實驗及結果 32 4.1 實驗說明 32 4.2 實驗結果 35 4.2.1 代表性數位影像 35 4.2.2 柏克萊影像資料庫 43 第 5 章 結論與未來展望 50 5.1 結論 50 5.2 建議及未來方向 51 參考文獻 52 附錄:符號表 55 | |
| dc.language.iso | zh-TW | |
| dc.subject | 影像雜訊 | zh_TW |
| dc.subject | 雜訊估計 | zh_TW |
| dc.subject | 高斯雜訊 | zh_TW |
| dc.subject | 影像分割 | zh_TW |
| dc.subject | 正規化剪切 | zh_TW |
| dc.subject | 超級像素 | zh_TW |
| dc.subject | Gaussian noise | en |
| dc.subject | image noise | en |
| dc.subject | noise estimation | en |
| dc.subject | superpixel | en |
| dc.subject | normalized cut | en |
| dc.subject | image segmentation | en |
| dc.title | 利用超級像素分類技術估計數位影像之高斯雜訊 | zh_TW |
| dc.title | Gaussian Noise Estimation with Superpixel Classification in Digital Images | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 100-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 宋家驥,張瑞益 | |
| dc.subject.keyword | 影像雜訊,雜訊估計,高斯雜訊,影像分割,正規化剪切,超級像素, | zh_TW |
| dc.subject.keyword | image noise,noise estimation,Gaussian noise,image segmentation,normalized cut,superpixel, | en |
| dc.relation.page | 56 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2012-08-14 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 工程科學及海洋工程學研究所 | zh_TW |
| 顯示於系所單位： | 工程科學及海洋工程學系 | |
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