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研究生:王素霞
研究生(外文):Su-ShiaWang
論文名稱:色彩強化之低複雜度除霧演算法
論文名稱(外文):Low-complexity Defogging Algorithm with Color Enhancement
指導教授:陳培殷陳培殷引用關係
指導教授(外文):Pei-Yin Chen
學位類別:碩士
校院名稱:國立成功大學
系所名稱:資訊工程學系碩博士班
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:2012
畢業學年度:100
語文別:英文
論文頁數:40
中文關鍵詞:影像除霧光圈效應色彩強化
外文關鍵詞:image defogginghalo effectcolor enhancement
相關次數:
  • 被引用被引用:1
  • 點閱點閱:452
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  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:1
目前的監視器或障礙物偵測等電腦視覺應用系統中,常會因為天候不佳,造成所擷取的影像能見度低,色彩也不鮮明,而這些因素也可能會影響系統運作的可靠性。因此,如何將受到霧氣干擾的影像還原成較清晰的影像,是一個重要的議題。至今有很多關於影像除霧的演算法被提出,但是這些方法的複雜度很高,處理一張影像需要花費很多時間,無法直接應用在一些高效能需求的系統中。因此,一個低複雜度且能有效地強化影像能見度的除霧技術是不可或缺的。
在本論文中,我們提出了一個新的色彩強化低複雜度除霧演算法。這個除霧演算法主要分成三大步驟:(1)根據輸入影像的亮部以及暗部估計出一個適應性的大氣光(atmospheric light)值。(2)利用差質性的透射平面(transmission)估計方法來偵測物件的邊緣並且減低光圈效應。(3)使用色調映射(tone mapping)及亮度調整的方法來優化還原後的影像。本方法執行速度快,處理一張600×400的影像只需花0.2秒。從和其他方法的比較結果可得知,我們的方法不論在能見度以及色彩強化上都有很好的表現。
In some computer vision applications, such as monitors and obstacle detection systems, the visibility and color of the scene taken by these devices might be severely degraded due to bad weather conditions such as fog or haze. Therefore, defogging technology has becomes an important issue. Nowadays, there are a lot of papers that discuss how to recover a clear image from a foggy one, but these methods are often so complex that they need much more time to process an image. For this reason, a good image defogging technique that is low-complexity is crucial.
In this paper, we bring up a low-complexity defogging algorithm with color enhancement. There are three main steps to this method. First, we calculate an the value of atmospheric light from the lighter group and the darker group of an input image. Second, we use differential transmission estimation to detect edges and ease the halo effect. Finally, we employ tone mapping and illumination adjustment to optimize restored images. The execution time of our method is faster than other papers. It only takes about 0.2 sec for processing a 600×400 image. Furthermore, we know our resulting images are better in both visibility and color enhancement from comparison.
摘要 i
Abstract ii
Acknowledgements iii
Contents iv
List of Tables v
List of Figures vi
Chapter 1. Introduction 1
Chapter 2. Background 4
2.1. Optical Model 4
2.2. Dark Channel Prior Algorithm 6
Chapter 3. Proposed Haze Removal Method 9
3.1. The Atmospheric Light Estimation 10
3.2. The Transmission Estimation 17
3.3. Recovering the Scene Radiance 22
Chapter 4. Results 23
Chapter 5. Conclusions 37
Reference 38

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