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標題: | 深度類神經網路實現快速共軛焦雷射掃描顯微鏡縮減隨機取樣影像還原演算法 A Deep Neural Network for Fast Confocal Laser Scanning Microscopy Imaging Recovery Algorithm from Random Undersampling Measurements |
作者: | Kuang-Yao Chang 張光耀 |
指導教授: | 傅立成 |
關鍵字: | 共軛焦雷射掃描顯微鏡,壓縮感知,深度學習,殘差學習,卷積神經網絡, Confocal laser scanning microscopy,Compressive sensing,Deep learning,Convolutional neural network,Residual learning, |
出版年 : | 2017 |
學位: | 碩士 |
摘要: | 共軛焦雷射掃描顯微鏡是一種高精度非破壞性之光學檢測系統。其可用於構建生物細胞與微米或次微米等級之工程材料的三維輪廓。近年來,壓縮感測的技術廣泛應用於顯微鏡系統,壓縮感知技術是透過減少重建正確訊號所需的採集數據量,所以將其應用於掃描式顯微鏡可降低總取樣路徑進而提升掃描速度。然而,在共軛焦雷射掃描顯微鏡應用中使用的壓縮感知還原算法是基於迭代式優化方法,其計算複雜度相對較高而需要較久的還原運算時間。在本篇著作中,提出了一種基於非迭代式的深度殘差卷積神經網絡壓縮感知還原演算法,我們所提出的方法不僅降低運算的時間,也提升了重建圖像的品質,並且我們可以利用單一的訓練模型來還原以不同縮減取樣率所蒐集的影像訊號。本篇著作亦提供數值化的分析結果,並且驗證我們提出的方法在不同的縮減取樣率下勝過其他壓縮感知還原演算法。為了解決不均勻的樣本資訊密度的問題,我們進一步提出了適應性縮減取樣率策略來調整不同局部地區的縮減取樣率。最後,透過重建以隨機掃描模式採集之共軛焦雷射顯微鏡的真實量測數據,驗證了我們提出的方法使用於顯微鏡掃描之實際應用的可靠性。 Confocal laser scanning microscopy (CLSM) is a powerful non-destructive optical inspection system in high precision measurement technology. CLSM can be used to construct three-dimensional profile of biological cells or micro and sub-micro engineering materials. Recently, compressive sensing (CS) is applied to CLSM system for high speed scan by reducing the amount of sampled data required to reconstruct an accurate image. However, the CS recovery algorithm employed in CLSM applications is iteration-based optimization method of which computation complexity is relatively high. In this work, a non-iteration-based deep residual convolutional neural network compressive sensing reconstruction (DRCNN-CSR) framework in end-to-end manner is proposed. Not only the computation time but also the quality of reconstructed image is greatly improved with this algorithm, and our method has an ability to recover images sampled under multi-undersampling rates (USRs) in single trained model. The quantitative comparisons with state-of-the-art CS recovery algorithms are provided, and the experiment results demonstrate that our proposed method outperforms the others under a wide range of under-sampling rates. Furthermore, in order to deal with the uneven sample information density problem, we also propose an adaptive undersampling rate strategy to adjust the under-sampling rate in different local areas. At the end, by the reconstructions of the real CLSM measured data in random scanning pattern, the recovery robustness of our model is validated for fast CLSM imaging application. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/67401 |
DOI: | 10.6342/NTU201702346 |
全文授權: | 有償授權 |
顯示於系所單位: | 電機工程學系 |
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