臺灣博碩士論文加值系統

數位影像容易受到雜訊影響，因此影像濾波器常被視成影像處理系統中的前置處理。若影像有嚴重損毀或高雜訊時，傳統的影像濾波器通常無法處理得很好。因此，本論文利用模糊系統可以容忍雜訊的優點，改善傳統的中值濾波器，再利用螞蟻演算法調整模糊影像濾波器的參數，期能使濾波器能夠達到較佳的性能。螞蟻演算法(Ant Colony Optimization)是利用螞蟻在搜尋食物時在路徑上所留下的費洛蒙(pheromone)濃度的高低所發展出來的最佳化方法，隨著時間的推進，長路徑的費洛蒙濃度較低，短路徑的費洛蒙濃度較高，費洛蒙濃度較高的路徑就會被選擇。螞蟻演算法包括費洛蒙濃度的計算、揮發係數的設定、狀態轉移概率的計算等等。除此之外，螞蟻演算法擁有全域與局部兩種搜尋方式，因此有多點搜尋、快速收斂的特性。本文係結合模糊濾波器與螞蟻演算法之特點，以改善整體影像品質，最後影像濾波結果顯示本文所提方法之有效性與可行性。

The digital images are easily affected by the noises; hence the image filters are often regarded as pre-processing of image processing system. If the image has serious damage or high-noise, the traditional image filters are usually unable to handle well. Therefore, this thesis utilizes the advantages of the fuzzy system to improve the traditional median filter, and then use ant colony optimization (ACO) algorithm to adjust the parameters of fuzzy image filter and make the filter to achieve better performance. ACO algorithm is an optimal method that developed from pheromones concentration level on the path. With the time past, the concentration of pheromones on the long path is lower, the concentration of pheromones of the short path is higher, then the shortest path will be selected. ACO algorithm includes the calculation of the pheromone concentration, the evaporation coefficient set, and the state transition probability calculation. In addition, ACO algorithm possesses the global and local search capability. Therefore, the ACO algorithm has the characteristics of muti-point search and fast convergence. This thesis combines the advantages of the fuzzy filter and ACO algorithm to improve the overall image quality. The final image filtering results demonstrate the effectiveness and feasibility of the proposed method.

Abstract (Chinese)Ⅰ
Abstract (English)Ⅱ
Acknowledgment Ⅲ
Contents Ⅳ
List of Figures V
List of Tables VII
Chapter 1. Introduction
1.1 Background of Research 1
1.2 Motivation 3
1.3 Thesis Organization 5
Chapter 2. Modified Fuzzy Multilevel Median Filter
2.1 Median Filter 6
2.2 Multilevel Median Filter 8
2.3 Fuzzy Multilevel Median Filter 10
2.4 Modified Fuzzy Multilevel Median Filter 12
Chapter 3. ACO Algorithm based Modified Fuzzy Multilevel Median Filter (MFMMF)
3.1 ACO Algorithm 19
3.2 Design of the MFMMF by ACO Algorithm 27
Chapter 4. Experimental Results 32
Chapter 5. Conclusions and Future Works
5.1 Conclusions 49
5.2 Future Works 50
References 51

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