臺灣博碩士論文加值系統

本論文提出一個基於多重策略進化規劃演算法之模糊相似量測法以達到彩色影像復原的混合型影像過濾器。首先，本論文提出一個多重方法論的進化規劃演算法 (MMEC) 來解決多重目標的最佳化問題，然後提出一個基於相似量測法的混合型影像過濾器以去除雜訊，最後藉由 MMEC 來顯示在相似量測法內的模糊集合以基於經驗的方法來建構是接近最佳的。實驗結果顯示基於模糊相似量測法的混合型影像過濾器可以比受限於函數形狀的傳統向量過濾器以及雙向過濾器達到更佳的過濾品質。此提出的過濾器能夠有效地復原受到脈衝雜訊、高斯雜訊、混合雜訊干擾的彩色影像。

A multi-methodology evolutionary computation and fuzzy similarity measure based hybrid image filter for color image restoration is proposed in this thesis. First, a multi-methodology evolutionary computation (MMEC) is proposed for multi-objective optimization problems. Then, a hybrid image filter with fuzzy-based similarity measure is proposed for noise reduction. Finally, an experience-based construction of fuzzy sets in the similarity measure has been shown as near-optimized via MMEC and is applied to color image restoration. The experimental results show that the proposed fuzzy similarity measure based hybrid image filter can achieve better filtering quality than the classical vector filters and the bilateral filter which are restricted by the shapes of functions themselves. The proposed filter is effective to restore color images contaminated by impulse noise, Gaussian noise, and mixed noise.

摘要 ... I
Abstract ... II
誌謝 ... III
Contents ... IV
List of Tables ... VIII
List of Figures ... IX
Chapter 1. Introduction ... 1
1.1. Survey of Image Restoration ... 1
1.1.1. Bilateral Filtering ... 1
1.1.2. Impulse Detection ... 2
1.1.3. Noise Estimation ... 3
1.1.4. Vector Image Filtering ... 3
1.1.5. Hybrid Filters ... 4
1.1.6. Neuro-fuzzy Filtering ... 4
1.1.7. Genetic-based Filtering ... 5
1.2. Motivation ... 6
1.3. Dissertation Organization ... 6
Chapter 2. Background ... 9
2.1. Weighted Averaging Filter ... 9
2.2. Fuzzy System ... 11
2.2.1. Fuzzy Set ... 11
2.2.2. Fuzzy Controller ... 11
2.3. Evolutionary Programming ... 13
2.4. Chaos-evolutionary-programming Algorithm ... 13
2.4.1. Population Initialization ... 14
2.4.2. Objective Function ... 15
2.4.3. Fitness Function ... 15
2.4.4. Probability Function ... 15
2.4.5. Population Mutation ... 16
2.4.6. Population Selection ... 17
2.4.7. Population Penalty ... 17
2.5. Chaos Optimization Algorithm ... 17
Chapter 3. Multi-methodology Evolutionary Computation ... 20
3.1. Population Initialization ... 20
3.2. Population Evaluation ... 21
3.3. Population Mutation ... 21
3.3.1. PFS based mutation ... 22
3.3.2. HS mutation ... 22
3.4. Population Crossover ... 23
3.4.1. Extrapolation ... 23
3.4.2. Interpolation ... 24
3.5. Population Selection ... 25
3.6. Performance of the Proposed Evolutionary Computation ... 25
Chapter 4. Models of Different Noises ... 31
4.1. Impulse Noise ... 31
4.2. Additive Noise ... 32
4.3. Mixed Noise ... 32
Chapter 5. Hybrid Filtering Process ... 33
5.1. Fuzzy Neighbor Filter ... 33
5.2. Fuzzy Range Filter ... 35
Chapter 6. Fuzzy Number Construction ... 36
6.1. Preprocessing of Initialization ... 36
6.2. Construction of Fuzzy Numbers ... 37
Chapter 7. Experimental Results ... 40
7.1. MMEC to Color Image Restoration ... 40
7.2. Impulse Noise Cancellation ... 43
7.3. Additive Noise Suppression ... 45
7.4. Mixed Noise Filtering ... 47
Chapter 8. Conclusion ... 51
Reference ... 53
Appendix A ... 56
自述 ... 57

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