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研究生:黃士峰
研究生(外文):Shi-Feng Huang
論文名稱:適應於昏暗環境中視訊雜訊衰減之研究
論文名稱(外文):The Study on Video Noise Reduction Method in The Dusk Environment
指導教授:陳昭和
指導教授(外文):Thou-Ho Chen
學位類別:碩士
校院名稱:國立高雄應用科技大學
系所名稱:電子與資訊工程研究所碩士班
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:2007
畢業學年度:95
語文別:中文
中文關鍵詞:雜訊衰減低光度(低對比)視訊處理
外文關鍵詞:noise reductionlow light level(low contrast)video process
相關次數:
  • 被引用被引用:3
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  • 下載下載:59
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隨著科技的發展,越來越多人開始利用影像/視訊的資訊應用到許多影像/視訊處理上,如機器人視覺、物體追蹤…等等。然而,往往視訊在擷取與傳輸的過程中,常常由於外在環境與器材內部電子元件的影響而產生非自然的資訊,這些非自然的資訊我們皆稱為雜訊。影像受汙染後會影響其後續處理的效果,例如影像切割、影像壓縮、人臉辨識等,因此,雜訊衰減在視訊處理中是一件很重要的任務。針對高對比視訊的雜訊衰減方法,在現今已有許多的方法被提出且效果也不錯。但是,在低對比視訊中的雜訊衰減方法相對下就比較困難地被實現。昏暗環境下的雜訊通常涵蓋眾多的種類,常見的有高斯雜訊(Gaussian Noise)、脈衝雜訊(Impulse Noise)、假色雜訊(False Color Noise)等,其中最令人感到不悅的是視訊畫面往往呈現粒子般跳動的雪花現象。為了改善上述,本論文提出了一套適應於昏暗環境下雜訊衰減的方法,整體架構主要利用移動偵測劃分背景資訊及前景資訊,分別地作不同的處理動作。經實驗結果得知,雖然運算上會比傳統方法來的複雜些,但是,我們所提的方法效果呈現比較好。經雜訊衰減後,除了能保留其邊緣、細節紋理處,也可達到即時處理的效能。不僅提高視訊品質,進而可增加一些後續處理的效能。
Many methods for noise reduction perform well for high contrast image sequences. However, the results of noise reduction are not good enough, when these methods apply in the dusk environment. Noises in low light level images are usually caused by thermal noise in the electronic circuitry inside the camera and low sensitivity cause the mis-displayed value on the R, G, and B planes. The noises look similar to Gaussian Noise, False Color Noise and Impulse Noise when discovered in a still frame. However, they look like snow flower in a video sequence. This paper presents a novel video noise reduction method for noises caused by photograph in the dusk environments. Our method includes segmentation technology and spatio-temporal filtering. Via applying the changing detection based method, the background and foreground are able to be separated. We use temporal average filter to recover the background and use spatial filter to recover the foreground. According to the experimental result, our method performs the following advantages: 1. the details of the signal and the textures will be preserved and the noises will be reduced. 2. high compressing ratio and real-time process can be achieved after the processes.
摘要
ABSTRACT
誌謝
目錄
表目錄
圖目錄

第一章 緒論
1.1研究背景
1.2研究動機與方向
1.3論文組織

第二章 數位影像表示及雜訊簡介
2.1 前言
2.2 數位影像表示法
2.3 數位影像處理基本步驟
2.4 影像雜訊簡介
2.4.1 高斯雜訊(Gaussian Noise)
2.4.2 脈衝雜訊(Impulse Noise)
2.4.3 假色雜訊(False Color Noise)
2.4.4 均值雜訊(Uniform Noise)
2.5 影像濾波定義

第三章 典型的視訊雜訊衰減方法介紹
3.1 無利用移動補償的濾波器
3.1.1 沒有利用移動補償的時間域濾波器
3.1.2 沒有利用移動補償的空間-時間域濾波器
3.1.2.1 Video alpha trimmed mean filter
3.1.2.2 The K nearest neighbor image sequence filter
3.2 有利用移動補償的濾波器
3.2.1 有利用移動補償的時域濾波器
3.2.2 有利用移動補償的空間-時域濾波器
3.2.2.1 Spatio-Temporal Separable Data-Dependent Weighted Average Filter
3.2.2.2 Effective Video Noise Reduction Based on Spatial-Temporal Filtering

第四章 低照度下視訊雜訊衰減方法介紹
4.1 夜間雜訊介紹
4.2 夜間視訊雜訊特徵
4.3 昏暗環境下影像雜訊衰減方法
4.3.1 早期針對LLL(low light level)影像雜訊衰減方法
4.3.2 近期針對LLL(low light level)影像雜訊衰減方法

第五章 適應於昏暗環境中視訊雜訊衰減之方法
5.1 絕對背景與雜訊臨界值
5.2 移動偵測(Motion Detection)
5.2.1 二值影像(Binary-Image)
5.2.1.1 時域標準差
5.2.2 形態運算
5.2.3 區域標記(Region Labeling)
5.2.4 判斷是否為移動物件
5.3 影像中未包含移動物件的處理
5.3.1 時間域濾波器
5.4 影像中包含移動物件的處理
5.4.1 空間域濾波器
5.4.1.1 K-nearest neighbor (K-NN)filter
5.4.1.2 鑽石型低通濾波器
5.5 實驗結果與比較

第六章 結論與未來展望
6.1 本研究方法之評析
6.2 未來發展方向

參考文獻
相關著作發表
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