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Student Number 974203048
Author Tsunghan Wu(吳宗翰)
Author's Email Address No Public.
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Department Information Management
Year 2009
Semester 2
Degree Master
Type of Document Master's Thesis
Language zh-TW.Big5 Chinese
Title A Computational Intelligence Based Approach with Complex Fuzzy Sets to Adaptive Image Noise Processing
Date of Defense 2010-06-23
Page Count 108
Keyword
  • adaptive image noise cancelling (AINC)
  • clustering
  • complex fuzzy set
  • complex neuro-fuzzy system
  • image restoration
  • PSO
  • RLSE
  • self-organization
  • Abstract In this thesis, we propose two novel adaptive filters, complex neuro-fuzzy system (CNFS) and self-organizing complex neuro-fuzzy system (SOCNFS), and apply them to the problem of adaptive image noise cancelling (AINC). Complex fuzzy sets (CFS) and Takagi-Sugeno (T-S) fuzzy If-Then rules are used to shape the structure of both the CNFS and SOCNFS. A CFS is the fuzzy set whose membership is complex-valued state within the unit disk in complex plane. We devise a hybrid optimization method to adapt the adaptive filters for the AINC problem. The hybrid learning method is called the PSO-RLSE method, including the well-known particle swarm optimization (PSO) method and the famous recursive least square estimation (RLSE) method. They cooperate in hybrid way during the learning process for the adaptive filters. The PSO is used to update the parameters of premise part of the filters while the consequent part is updated by the RLSE. The PSO-RLSE learning method is very efficient for fast learning. The proposed CNFS and SOCNFS filters possess excellent nonlinear mapping ability because CFS can bring in complex memberships in fuzzy inference computing for input-output mapping capability. On the other hand, with the mechanism of self-organization, the SOCNFS can generate fuzzy rules in the form of clusters and learn its parameters by the stimulation of input/output training data to have its initial If–Then rules for application. In the AINC application, the proposed CNFS and SOCNFS can perform indirect function approximation to mimic the dynamic behaviour of unknown noise channel in such a way that a corrupted image may be adaptively restored as clear to its original version as possible. Few examples with several images are used to test the proposed approachs, by which excellent performance for image restoration has been observed.
    Table of Content 中文摘要i
    英文摘要ii
    誌謝iii
    目錄iv
    圖目錄vi
    表目錄x
    第一章、 緒論1
    1.1研究背景1
    1.2研究方法與動機1
    1.3研究目的4
    1.4論文架構5
    第二章、 文獻探討6
    2.1數位影像資料格式與取樣量化6
    2.2常用雜訊消除濾波器9
    2.3適應性雜訊消除問題12
    2.4影像品質評估機制15
    第三章、適應性濾波器設計方法17
    3.1複數類神經模糊適應性濾波器17
    3.1.1 複數模糊集合17
    3.1.2 T-S模糊推理系統架構19
    3.2 自我組織複數類神經模糊適應性濾波器23
    3.2.1 以分群為基礎的自我組織機制23
    3.2.2 模糊C均值分裂演算法26
    3.3 複合式學習演算法29
    3.3.1 粒子群最佳化演算法29
    3.3.2 遞迴最小平方估計法30
    3.3.3 PSO-RLSE複合式學習演算法32
    第四章、 適應性濾波器應用於影像雜訊消除34
    第五章、 CNFS與SOCNFS進行影像雜訊消除38
    5.1應用CNFS適應性濾波器解決AINC問題38
    5.2應用SOCNFS適應性濾波器解決AINC問題61
    5.3實驗數據分析與討論88
    第六章、結論與未來工作92
    參考文獻93
    簡歷96
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    Advisor
  • Chunshien Li(李俊賢)
  • Files
  • 974203048.pdf
  • approve in 2 years
    Date of Submission 2010-07-26

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