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研究生:邱垂汶
研究生(外文):Chui-Wen Chiu
論文名稱:使用視覺高頻增強濾波器之高品質超解析度影像與視訊研究
論文名稱(外文):High Quality Spatial Resolution Enhancement using HHEF for Image and Video
指導教授:沈岱範
指導教授(外文):Day-Fann Shen
學位類別:碩士
校院名稱:國立雲林科技大學
系所名稱:電機工程系碩士班
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:2004
畢業學年度:92
語文別:中文
論文頁數:88
中文關鍵詞:頻域分析超解析度解析度增強區塊比對影像放大高頻增強濾波器(HHEF)亮度校正(IC)
外文關鍵詞:super resolutionimage enlargementIntensity correction (IC)High Frequency Emphasis Filter (HHEF)frequency domain analysisblock matchingresolution enhancement
相關次數:
  • 被引用被引用:6
  • 點閱點閱:246
  • 評分評分:
  • 下載下載:59
  • 收藏至我的研究室書目清單書目收藏:0
高解析度影像的意義在於影像中的像素密度較高能夠顯示更多細微的資訊,這在醫學上、犯罪偵測、衛星影像等應用領域都非常的有用。利用數位影像處理技術來克服影像和視訊在硬體上的空間解析度限制,這一類技術稱之為超解析度(Super-Resolution)、影像放大(Image Enlargement)或解析度增強(Resolutiuon Enhancement)。大部分的超解析度研究都專注於內插後所造成的邊緣(edge)和紋理(texture)區域的模糊效應,主要都是希望使影像看起來較為清晰。在本論文中,我們分別在影像和視訊上提出超解析度演算法。
在影像超解析度部分,我們分析並將高密度感測器獲得之高解析度影像與低感測器密度所獲得之退化過低解析度影像間的對應關係模組化。以退化模型(degradation model)為基礎,我們提出一個用來還原影像中受到衰減之高頻成分的高頻增強濾波器(HHEF)。我們接著導出在退化過程中的亮度校正關係(Intensity Correction, IC),利用IC使HHEF的增益能夠受到限制。我們也分析和評估本文所提之演算法的性能與其極限。在真實影像上所做的實驗顯示,利用本文所提之HHEF處理後的影像邊緣和紋理區域都獲得大幅的改善(感官和PSNR上);相較之下,其他方法大部分都只強化邊緣部分。
在視訊超解析度部分,我們利用視訊中前後多張低解析度影像來提供目前高解析度影像所缺少的像素資訊方法。在以多張畫面為基礎的視訊超解析度中,我們以分段克服(divide amd conquer)方法來分析每一個過程中的效能和極限,並提出不同的搜尋策略和相似性評估準則來改善演算法的精準度。並應用本文所提之HHEF和IC影像超解析度來進一步在感官和PSNR上改善影像品質。
Images with higher resolution, able to display more details of the scene, are very desirable in many serious applications such as medical imaging, law-enforcement, satellite imaging, space probing etc. The image processing techniques that exceed the hardware limitation to increase the spatial resolution for images or videos are referred to as super-resolution (or image enlargement, or resolution enhancement) techniques. Most super resolution researches focus on the elimination of blurs around edges and texture areas after the interpolation, such that the image looks sharper. In this thesis, we propose super-resolution algorithms for both still images and videos.
In the image super-resolution, we analyze and model the relationship between a high-resolution image (obtained by a higher sensor density) and the corresponding degraded low-resolution image (obtained by lower sensor density). Based on the degradation model, we propose a high frequency emphasis filter (HHEF) to restore the suppressed high frequency components in the image. We proceed to derive the Intensity Correction (IC) relationship in the degradation process as the constraint of HHEF gain. We also analyze and evaluate the performance and limitations of the proposed approach. Experiments on real images show that both edges and texture areas are enhanced significantly (perceptually and PSNR) by the proposed HHEF, while most other super-resolution methods enhance only the edges.
In the video super-resolution, we adopt the multi-frame based approach, where previous and future low-resolution frames are used to estimate the increased pixels in the current high-resolution frame. We take the divide and conquer approach to analyze the effectiveness and limitations of each process in the multi-frame based video super-resolution algorithm and make improvements accordingly on the search strategy and match criterion. The HHEF and IC developed in still image super-resolution are apply to further
Improve the image quality both perceptually and PSNR.
目 錄
中文摘要 i
英文摘要 iii
誌謝 v
目錄 vi
表目錄 viii
圖目錄 ix

第一章 緒論 1
1.1 研究背景 1
1.2 研究動機與研究目標 3
1.3 研究方法 4
1.4 重要結果與本論文貢獻 4
1.5 各章節提要 4
第二章 背景 6
2.1 靜態影像超解析度 6
2.2 動態影像超解析度 7
2.3 影像評估 8
2.3.1 數值差異評估法 9
2.3.2 視覺差異評估法 11
第三章 高頻增強濾波器於超高解析度靜態影像之應用 13
3.1 問題描述 13
3.2 回顧常用增加解析度的方法 14
3.2.1 常用的多項式內插 14
3.2.2 沿著邊緣方向內插的靜態影像超解析度 17
3.2.3 以樣本為基礎的靜態影像超解析度 19
3.3 以人眼視覺特性為基礎之高頻增強濾波器演算法 20
3.4 實驗結果與性能評估 26
3.4.1 moon 538x464, 256bits, 灰階影像測試 27
3.4.2 Lena 256x256, 256bits, 灰階影像測試 32
3.4.3 Baboon 256x256, 256bits, 彩色影像測試 34
3.4.4 moon 538x464, 256bits, 以Bicubic為基礎灰階影像測試 35
3.5 結論 41
第四章 動態影像超解析度 42
4.1 退化模型簡介 42
4.2 問題描述 43
4.3 文獻回顧 44
4.3.1 統計學方法 44
4.3.2 非均勻空間內插方法 45
4.4 問題分析 46
4.4.1 理想狀況 46
4.4.2 半理想狀況 49
4.4.3 實際狀況 50
4.5 以人眼視覺特行為基礎之超解析度演算法 50
4.6 實驗結果與性能評估 63
4.6.1 Translation Motion Model灰階影像序列2x2放大效果 63
4.6.2 Translation Motion Model–彩色影像序列2x2放大效果 67
4.6.3 Similarity Motion Model–灰階影像序列2x2放大效果 70
4.6.4 Similarity Motion Model–灰階影像序列2x2放大效果 74
4.7 減少演算法計算量的方式 77
4.8 結論 77
第五章 結論與未來研究方向 79
附錄A、影像退化模型 80
參考文獻 83
作者簡歷 87
參 考 文 獻

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