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研究生(外文):Shi-Feng Huang
論文名稱(外文):The Study on Video Noise Reduction Method in The Dusk Environment
指導教授(外文):Thou-Ho Chen
外文關鍵詞:noise reductionlow light level(low contrast)video process
  • 被引用被引用:3
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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.

第一章 緒論

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 Video alpha trimmed mean filter 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 Spatio-Temporal Separable Data-Dependent Weighted Average Filter 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) 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 K-nearest neighbor (K-NN)filter 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

[1] Antonio Borneo, Lanfranco Salinari, and Daniele Sirtori, 2000, “ An Innovative Adaptive Noise Reduction Filter for Moving Pictures Based on Modified Duncan Range Test ”, ST-Journal of System Research, Vol. 1, No. 1.
[2] A. Bosco, M. Mancuso, S. Battiato, and G. Spampinato, 2002, “ Temporal noise reduction of Bayer matrixed video data ”, IEEE International Conference on Multimedia and Expo. 2002 Proceedings (ICME '02), Vol. 1, Pages: 681 - 684, Aug. 26-29.
[3] Byung Cheol Song and Kang Wook Chun, 2004, “ Motion-Compensated Temporal Pre-Filtering for Noise Reduction in a Video Encoder ”, IEEE International Conference on Image Processing, October 24–27.
[4] Brailean, J.C.; Kleihorst, R.P.; Efstratiadis, S.; Katsaggelos, A.K.; Lagendijk, R.L., “ Noise reduction filters for dynamic image sequences: a review ”, Proceedings of the IEEE, Vol. 83, Issue: 9, Pages: 1272-1292, Sept. 1995
[5] V. Zlokolica, W. Philips, and D. Van De Ville, 2002, “ Robust non-linear filtering for video processing ”, 14th International Conference on Digital Signal Processing, DSP 2002., Vol. 2, Pages: 571-574, July 1-3.
[6] Kouji Miyata, and Akira Taguchi, 2002, “ Spatio-temporal separable data-dependent weighted average filtering for restoration of the image sequences ”, IEEE International Conference on Acoustics, Speech, and Signal Processing, 2002. Proceedings. (ICASSP '02)., Vol. 4, Pages: 3696-3699, May 13-17.
[7] M. Meguro, A. Taguchi and N. Hamada, 1999, “ Data-dependent weighted average filtering for image sequence restoration ”, IEICE Trans. Vol. J82-A, no.10, Pages:1623-1632, Oct. (in Japanese)
[8] J. M. Boyce, 1992, “ Noise reduction of image sequences using adaptive motion compensated frame averaging ” IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP-92., Vol. 3 , Pages: 461 - 464, March 23-26.
[9] Young Huang and Lucas Hui, 2003, “ An adaptive spatial filter for additive Gaussian and impulse noise reduction in video signals ”, Proceedings of the 2003 Joint Conference of the Fourth International Conference on Information, Communications and Signal Processing, 2003 and the Fourth Pacific Rim Conference on Multimedia, Vol. 1, Pages: 523 - 526 , Dec. 15-18.
[10] K. Jostschulte, A. Amer, M. Schu, and H. Schroer, 1998, “ Perception Adaptive Temporal TV-Noise Reduction Using Contour Preserving Prefilter Techniques ” , IEEE Trans. on Consumer Electronics (TCE), Vol. 44, no. 3, Pages: 1091 - 1096, Aug.
[11] J. O. Drewery, R. Storey, and N. E. Tanton, 1984, “ Video Noise Reduction ”, BBC Research Department Report, July.
[12] T.S. Huang, Ed., 1981, Image Sequence Analysis. Berlin: Springer-Verlag, pp.289-290.
[13] H.B. Mitchell and N. Mashkit, 1992, “Noise smoothing by a fast k-nearest neighbour algorithm,” Signal Processing: Image Communication, vol. 4, pp.227-232.
[14] Seong-Won Lee, Vivek Maik; Jihoon Jang, Jeongho Shin and Joonki Paik, 2005,“Noise-adaptive spatio-temporal filter for real-time noise removal in low light level images”, IEEE Transactions on Consumer Electronics, Vol. 51, Issue2, pp.648-653, May.
[15] Zhang Zheng; Shen Jiali; Wang Xiutan; Ma Zhange; Peng Yingning, 2000,“Digital image processing system for LLLTV”, Signal Processing Proceedings, WCCC-ICSP 2000, 5th International Conference on Vol. 2, pp.955-962, 21-25 Aug.
[16] Bai Lianfa, Chen Qian, Gu Guohua and Zhang Baomin, 1998, “Time-space noise processing technique for low light level (LLL) image”, Signal Processing Proceedings, 1998. ICSP '98, 1998 Fourth International Conference on Vol.2, pp.1052-1055, 12-16 Oct.
[17] Chen Qian, Bai Lianfa and Zhang Baomin, 1996, “Real-time adaptive noise processing in low light level images”, Signal Processing, 1996., 3rd International Conference on Vol.1, Page(s):606–609, 14-18 Oct.
[18] 林啟明,2005,應用於模糊切換濾波器技術於影像脈衝式雜訊去除之研究,國立東華大學,電機工程學系,碩士論文。
[19] RC. Gonzalez, RE. Woods, Ed., 2002, Digital Image Processing. Beijing:Publishing House of Electronics Industry.
[20] 吳明坤,2005,基於時空濾波處理之有效視訊雜訊衰減之研究,國立高雄應用科技大學,電子工程系,碩士論文。
[21] 許詠泰,2003,應用於監視系統之強健式整合性的即時移動物體偵測與追蹤演算法,元智大學,機械工程研究所,碩士論文。
[22] S.D. Jean, C.M. Liu, C.C. Chang, and Z. Chen, 1994, “A New Algorithm and its VLSI Architecture Design for Connected Component Labeling,” IEEE International Symposium on, Vol. 2, pp. 565-568.
[23] Shao-Yi Chien, Yu-Wen Huang, Bing-Yu Hsieh, Shyh-Yih Ma, and Liang-Gee Chen, 2004, “Fast Video Segmentation Algorithm with Shadow Cancellation, Global Motion Compensation, and Adaptive Threshold Techniques,” IEEE Trans. on Circuits and System for Video Technol., vol. 6, pp. 732-748, no. 5, Oct.
[24] 陳炤宇,2006,基於適應性像素關聯性中值濾波器的脈衝雜訊衰減之研究,國立高雄應用科技大學,電子工程系,碩士論文。
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