研究生: |
蔣暉岳 Hui-Yueh Chiang |
---|---|
論文名稱: |
基於 H-infinity優化控制器參數與神經網路加速還原影像動態模糊 Dynamic Image Deblurring with H-infinity Optimized Controller Parameters and Neural Network Acceleration |
指導教授: |
顏家鈺
Jia-Yush Yen |
口試委員: |
陳亮嘉
Liang-Chia Chen 林紀穎 Chi-Ying Lin 王富正 Fu-Cheng Wang 陳永耀 Yung-Yaw Chen |
學位類別: |
碩士 Master |
系所名稱: |
工程學院 - 機械工程系 Department of Mechanical Engineering |
論文出版年: | 2023 |
畢業學年度: | 111 |
語文別: | 中文 |
論文頁數: | 141 |
中文關鍵詞: | 強健控制 、粒子群最佳演算法 、運動模糊 、影像還原 |
外文關鍵詞: | Robust control, Particle swarm optimization, Motion blur, Image restoration |
相關次數: | 點閱:291 下載:9 |
分享至: |
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隨著科技的不斷進步,晶圓的尺寸逐漸擴大,並且自動化光學檢測應用也蓬勃發展,這使得晶圓檢測平台的運動控制性面臨更多挑戰。晶圓檢測平台是一套精密複雜的系統,其主要功能是在檢測物體快速移動時,透過拍攝影像並進行動態模糊還原。
在控制精密平台方面,本研究採用了現有的永磁同步線性馬達模型,並使用粒子群最佳化演算法以鑑別出運動平台各軸的受控場之傳遞函數。為了實現控制器的設計,我們採用了常數增益控制策略,我們採用了Tsai提出的鏈散射描述法(Chain scattering description)去設計常數增益的控制器,以達到系統的響應具有良好的穩定性和準確性。
影像還原方面,由於光學探頭共用給二維和三維的影像,因此二維影像將遇到色散的問題以及拍攝物體在移動下的動態模糊,本論文對二維影像進行運動影像演算法還原,使用Pan所提出的影像還原方法,將模糊的影像還原成清晰的影像。Pan的影像還原方法,將模糊影像的灰階值梯度分佈,透過影像先驗資訊(Image Prior)來成為影像還原模型的參數,以影像金字塔迭代的方式,找出影像的結構體再透過梯度灰階值分佈來得到模糊核(Blur Kernel),最後得到清晰影像。傳統的迭代法通常將會耗時許久,所以為了提升還原效率,透過監督式神經網路來提高還原效率。
平台在移動下所拍攝的模糊影像,經由影像還原模型處理後,將對結果使用Anish Mittal所提出的無參考圖項指標(Natural image quality evaluator)去做影像還原的客觀評分。
With the increasing size of wafers and the widespread use of automated optical inspection, motion control performance of wafer inspection platforms faces new challenges. These platforms are complex systems that capture images and restore motion blur while objects are moving rapidly.
To achieve precise platform control, this study utilizes particle swarm optimization algorithm to identify the transfer function of each axis of the motion platform based on the existing model of permanent magnet synchronous linear motors. A constant gain control strategy is then employed, using Tsai's chain scattering description method to design the constant gain controller, ensuring stable and accurate system response.
In terms of image restoration, the shared optical probe for 2D and 3D imaging introduces challenges such as chromatic dispersion and dynamic blur caused by object motion. This paper focuses on restoring motion-blurred 2D images using Pan's proposed method. Pan's method utilizes the gray value gradient distribution and image prior information as parameters for the restoration model. By iteratively processing image pyramids, the structural elements of the image are identified, and the blur kernel is obtained based on the gradient gray value distribution, resulting in clear images. To improve restoration efficiency, a supervised neural network is employed.
The image restoration model processes the blurry images captured during platform movement, and the results are objectively evaluated using Anish Mittal's no-reference image quality evaluator for image restoration.
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