臺灣博碩士論文加值系統

在戶外環境中攝影時，空氣中的介質會使光線衰減並更進一步降低影像品質，尤其在濃霧的環境中最為明顯。這些有霧的影像因失去大量的信息造成影像辨識系統無法從影像中辨識目標。為了克服影像品質衰減問題，本篇論文提出兩個影像除霧方法。第一個方法透過提出的模糊推論系統估測出光線衰減的變化，接著使用形態學與設計的類神經網路濾波器來消除光暈，大氣光計算方面透過模糊系統估測出的結果計算出大氣光影像並計算大氣光的顏色向量解決色偏問題。另一個方法則是利用模糊系統估測出不同的衰減結果並透過權重方法結合做計算，在大氣光的估測部分與前面方法相同。最後實驗結果將會與其他方法做比較。

The image quality will be reduced with the light attenuation by atmospheric particles when taking a photograph outdoors, especially in the environment with haze. Because the hazy images lose a lot of information, the image recognition systems cannot recognize the target in the image. In order to solve the problem of the image quality attenuation, there are two image dehazing methods proposed in this paper. To use the proposed fuzzy inference system is able to estimate the attenuation condition of the light. For the problem of the halo artifacts, this paper combines the morphology and the neural network to solve this problem. This paper proposes the average method to calculate the atmospheric light to solve the problem causing by the color cast. The other method is to use the fuzzy inference system to estimate the two different transmission maps which are combined by the weighted method to produce the recovered image. Finally, we demonstrate the experimental results and compare our proposed methods with other existing approaches.

摘要 I
Abstract II
目錄 VI
圖目錄 VIII
表目錄 XI
Chapter 1. 緒論 1
1.1研究動機 1
1.2物理原理 2
1.3文獻探討 5
1.4研究目標 10
1.5章節介紹 12
Chapter 2. 模糊理論與類神經網路之回顧 13
2.1模糊邏輯系統 13
2.2類神經網路 16
Chapter 3. 植基於模糊理論之類神經網路濾波器設計於影像除霧 20
3.1植基於模糊理論之傳輸平面估測 22
3.2以類神經網路濾波器實現光暈現象之消除 26
3.3大氣光顏色向量之均值估測法及影像還原 34
3.4實驗結果 37
3.4.1模糊法則數設計之比較 39
3.4.2比較其他除霧方法 43
Chapter 4. 植基於模糊理論之權重法設計於影像除霧 50
4.1植基於模糊理論之權重法 52
4.2實驗結果 54
4.2.1參數適應性之評估 54
4.2.2比較其他除霧方法 57
Chapter 5. 實驗與討論 64
Chapter 6. 結論與未來工作 67
參考文獻 69
個人資料 75
 
圖目錄
圖 1真實環境霧氣模型 3
圖 2影像還原結果 6
圖 3影像還原結果 7
圖 4影像還原結果 7
圖 5精煉傳輸平面結果 8
圖 6 引導濾波器 9
圖 7影像還原結果 10
圖 8一般影像除霧方法流程圖 11
圖 9 傳統集合與模糊集合 15
圖 10模糊推論系統 16
圖 11 S型函數曲線圖 18
圖 12類神經網路架構圖 18
圖 13植基於模糊理論之類神經網路濾波器流程圖 21
圖 14傳輸平面之模糊推論系統 22
圖 15模糊歸屬函數 23
圖 16比較遮罩大小 27
圖 17光暈現象 28
圖 18評估光暈結果 29
圖 19消除光暈結果 30
圖 20類神經網路濾波器 31
圖 21類神經網路濾波器學習曲線圖 32
圖 22濾波器輸出結果 33
圖 23計算大氣光影像 34
圖 24大氣光光源估測 36
圖 25模糊法則比較 40
圖 26模糊法則比較 41
圖 27模糊法則比較 42
圖 28比較其他除霧方法 44
圖 29比較其他除霧方法 45
圖 30比較其他除霧方法 46
圖 31比較其他除霧方法 47
圖 32比較其他除霧方法 48
圖 33比較其他除霧方法 49
圖 34植基於模糊理論之權重法流程圖 51
圖 35評估光暈結果 53
圖 36參數比較結果 56
圖 37光暈比較 57
圖 38比較其他除霧方法 58
圖 39比較其他除霧方法 59
圖 40比較其他除霧方法 60
圖 41比較其他除霧方法 61
圖 42比較其他除霧方法 62
圖 43比較其他除霧方法 63
圖 44比較消除光暈結果 65
 
表目錄
表 1濾波器參數表 32
表 2參數設定表 38
表 3優缺點比較表 66

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