臺灣博碩士論文加值系統

在本論文中，提出一個結合中值濾波與影像修補策略針對高密度脈衝雜訊影像之雜訊去除方法。首先利用影像灰階值直方圖的分布來分辨可能的雜點，接著針對判斷出的可能雜點計算該點周圍非雜訊點的數量然後按照數量之高低排序，做為移除雜訊的順序。當周圍雜訊點的數量大於一個特定的門檻值，影像修補策略將會作用來移除雜訊，反之則採用中值濾波法。基於四張標準測試影像，實驗結果演示在高密度脈衝雜訊環境中，提出的方法會優於其他最新發展的相關方法。

In this thesis, an effective median filter- and inpainting-based method for high density impulse noise removal is proposed. We first utilize the histogram distribution to identify the possible corrupted pixels. For each corrupted pixel, we count the number of uncorrupted neighboring pixels, and then a sorting process is applied to schedule the order of noise removal. For the current corrupted pixel, when the number of corrupted neighboring pixels is larger than the specified threshold, an inpainting technique is applied to remove the noise; otherwise, a median filter is applied. Based on four typical test images, experimental results demonstrate that for high-density impulse noise circumstance, the proposed noise removal method outperforms the state-of-the-art method by Hong et al. and some other related methods.

教授推薦書. . . . . . . . . . . . . . . . . . . . i
論文口試委員審定書 . . . . . . . . . . . . . . . ii
中文摘要 . . . . . . . . . . . . . . . . . . . iii
Abstract . . . . . . . . . . . . . . . . . . iv
誌謝 . . . . . . . . . . . . . . . . . . . . . v
Table of Contents . . . . . . . . . . . . . . . vi
List of Tables . . .. . . . . . . . . . . . . viii
List of Figures . . . . . . . . . . . . . . . . x
1 Introduction . . . . . . . . . . . . . . . . . 1
2 Impulse Noise Models and Detection . . . . . . 3
2.1 Noise Models . . . . . . . . . . . . . . . . 3
2.2 Detecting Stage. . . . . . . . . . . . . . . 4
3 Proposed Hybrid-based Noise Removal Method . . 7
3.1 Sorting Stage . . . .. . . . . . . . . . . . 7
3.2 Filtering Stage . . . . . . . . . . . . . . 8
4 Experimental Results . . . . . . . . . . . . . 13
4.1 Based on Noise Model 1 . . . . . . . . . . . 13
4.2 Based on Noise Model 2 . . . . . . . . . . . 15
4.3 Based on Noise Model 3 . . . . . . . . . . . 20
4.4 Based on Noise Model 4 . . . . . . . . . . . 22
4.5 Based on Noise Model 5 . . . . . . . . . . . 22
5 Conclusion and future work . . . . . . . . . . 33

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