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研究生：	林志宏 Lin, Zhi-Hong
論文名稱：	物件式時域濾波快速視訊雜訊去除方法 A Fast Video Noise Reduction Method by Object-Based Temporal Filtering
指導教授：	陳進興 Chen, Chin-Hsing
學位類別：	碩士 Master
系所名稱：	電機資訊學院 - 電腦與通信工程研究所 Institute of Computer & Communication Engineering
論文出版年：	2007
畢業學年度：	95
語文別：	英文
論文頁數：	64
外文關鍵詞：	temporal filtering, noise reduction, CCD camera
相關次數：	點閱：60 下載：1
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在多媒體通訊的時代裡，影像跟視訊扮演一個極重要的角色，因為可以提供我們很富豐的資訊。不過，無論攝影機或照相機是多麼的先進，也沒有影像或視訊是絕對完美的，因為影像會因雜訊的存在而品質下降，進而影響其後續處理的效果，因此，雜訊衰減在視訊處理中是一件很重要的任務。在數位影像及視訊中，雜訊主要在影像擷取（數位化）及傳輸期間出現。成像感測器的表現受很多因素影響，像是影像擷取期間週遭的環境情況，以及感測元件本身的品質。
利用移動補償的濾波有三項缺點：1. 演算法複雜，計算量大導致無法即時處理，2. 高度雜訊下，估測準確率會大大的降低，3. 方塊效應。沒有利用移動補償的濾波雖然有較快的執行速度，但都有一個致命的缺點：物體移動會產生殘影和影像輪廓邊緣模糊的現象。為了改善移動補償濾波的執行速度及沒有利用移動補償濾波的殘影和模糊邊緣的問題，本論文發展出一種能兼顧執行速度與不會有殘影和模糊邊緣的即時時域濾波技術來做雜訊衰減及視訊細節復原。基本想法是快速的判斷出靜態區域與動態區域，再以不同的濾波技術做處理，因此在衰減雜訊後，不但可以符合人眼視覺特性而提高視訊品質，提高壓縮比及降低硬體成本，以及提高濾波處理的速度效能。
本論文的實驗是使用CCTV所拍攝的真實影片作為測試影片，並以video alpha trimmed mean filter、simple frame averaging technique、combine switching median (SM) filter and easy temporal filter、the real-time adaptive interframe temporal comparison technique 做為比較對象。我們跟其他濾波方式比較有較快的執行速度與較少的複雜度；在改善壓縮比上，當使用“IndeoR Video 5.2 Compression Filter”壓縮時，在不同的影像序列中我們分別有15.33%、36.97%、48.01%、47.27%、33.41%、33.41%的改善；而當壓縮成MPEG2格式時，在不同的影像序列中我們分別有43.08%、50.65%、48.95%、48.42%、52.22%、44.75%的改善；在人眼視覺的比較上，我們處理過的影片有更完善的視覺效果。

In an ear of multimedia communication, images and videos play important roles because they can provide very rich information for us. However, no images and videos are absolutely perfect no matter how good the camera is, because they could be degraded by the presence of noise. Noises in digital images and videos arise from acquisition (digitization) and transmission. The performance of imaging sensors is affected by a variety of factors, such as environmental conditions during image and video acquisition and by the quality of the sensing elements themselves.
Generally, motion compensated Spatial-temporal filters and temporal filters have three shortcomings: (1) motion compensation is very complex and its computation load is too high to use. So, the filters which used motion compensation can not realize real-time video processing. (2) when videos are corrupted by high level noises, the motion estimation accuracy will be decreased greatly. (3) the filtered videos have disgusting blocking effect and blocking artifacts. Nonmotion compensated Spatial-temporal filters and temporal filters usually have faster speed performance but they have a fatal drawback: they may cause object-overlapped or object-blurred phenomenon in an image frame while an object is moving. In order to improve the speed of motion compensated filters and the object-overlapped or object-blurred phenomenon of nonmotion compensated filters, we propose a novel fast temporal filtering technique to reduce noise in this thesis. The basic idea is to use a fast method to differentiate between the stationary regions and the moving regions and filtering these regions in different ways. This filter not only can reduce video noise but also preserve video details effectively. And, it can enhance video to up to higher quality to improve compression ratio. Also, the filter is of low complexity and possesses high speed performance.
The test videos in our experiments are captured by CCTV. We compare the following filters: (1) video alpha trimmed mean filter, (2) simple frame averaging technique, (3) combining switching median (SM) filter and easy temporal filter, (4) the real-time adaptive interframe temporal comparison technique, (5) the proposed filter. The proposed filter has faster speed performance and smaller computation load than the other filters. On improving compression ratio, when we use “IndeoR Video 5.2 Compression Filter” to compress the processed videos, we have 15.33%, 36.97%, 48.01%, 47.27%, 33.41% and 33.41% improvements for the 6 different test videos respectively. When we use “bbMPEG” to compress the processed videos in MPEG2 format, we have 43.08%, 50.65%, 48.95%, 48.42%, 52.22% and 44.75% improvements for the 6 different test videos respectively. For the visual comparison, the proposed filter produces higher quality than all other filters compared.

摘要	i
Abstract	iii
誌謝	v
Contents	vi
Figure Captions	viii
Table Captions	x

Chapter 1 Introduction	1
1 Background	1
2 Motivation	2
3 Related Works	2
4 Thesis Organization	4

Chapter 2 Properties of Image Noise and Video Noise	5
1 Spatial Domain Noise	5
1.1 Impulsive Noises	5
1.2 Gaussian Noise	8
2 Temporal Domain Noises	10
2.1 Permanent Noises	10
2.2 Temporal Transient Noises	10
2.3 Temporal Intermediate Noises	10
3 Real Noise Caused by CCD-based Cameras	11

Chapter 3 Filters for Reducing Video Noise	16
1 Classification of Filtering Video Noises	17
2 Nonmotion Compensated Spatiotemporal Filtering	18
2.1 Video Alpha Trimmed Mean Filter	19
2.2 The K-nearest Neighbor Image Sequence Filter	20
3 Nonmotion Compensated Temporal Filtering	21
3.1 Easy Temporal Filter	21
3.2 Combining Switching Median (SM) Filter and Easy Temporal Filter	23
3.3 Real-Time Adaptive Interframe Temporal Comparison Technique	27

Chapter 4 The Proposed Algorithm for Reducing Video Noises	30
1 Description of Our New Video Processing Algorithm	30
2 Adjustment of Parameters	36
2.1 When the CCD Camera Is under the High Light Level.	36
2.2 When the CCD Camera Is under the Median Light Level.	38
2.3 When the CCD Camera Is under the Low Light Level.	39
3 Steps and Flow Chart	41

Chapter 5 Experimental Results and Discussion	43
1 The Comparisons of Filters under the High Light Level	44
2 The Comparisons of Filters under the Median Light Level	46
3 The Comparisons of Filters under the Low Light Level	49
4 The Comparisons of Filters under Mixed Situation	53
5 Analysis and Comparison	59

Chapter 6 Conclusion and Future Work	60
1 Conclusion	60
2 Future Work	61

References	62
                                    

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校內：2010-07-26公開
校外：2011-07-26公開

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