透過您的圖書館登入
IP:18.191.21.86
  • 學位論文

多頻譜影像應用於頻譜重建之研究

Multi-spectral Imaging for Spectral Reconstruction

指導教授 : 田仲豪

摘要


本文利用全數位的方式來建立多頻譜影像系統,藉由影像數位值重建其目標物的頻譜資訊。一開始利用頻譜儀與多通道彩色相機量測基礎色票之頻譜資訊與影像數位值,透過頻譜資訊與影像數位值之關係建立其數學模型,最後只需要使用相機拍攝物體,並將影像數位值輸入於轉換矩陣中,即可得到目標物的反射頻譜。在其還原過程中,為了降低頻譜資料量,本文提出利用主成分分析法來降低頻譜的維度,最後透過廣義反矩陣法,建立其影像數位值與頻譜資訊之間的轉換矩陣。我們針對基礎色票進行實驗,以驗證此數學模型之正確性,並分析與探討。論文最後,我們將頻譜重建技術,應用於辨別同色異譜之現象。

並列摘要


This study aims to develop a multi-spectral imaging system based on a fully digital method. This method can reconstruct the spectral reflectance from digital counts of images. Firstly, we setup spectroradiometer and CCD to measure the spectral reflectance and the digital counts of the training dataset. Then, we used Principal Component Analysis (PCA) to reduce both the dimension of database and computational effort. Form the spectral reflectance and digital counts, the system can establish a linear transformation as a mathematical model. Finally, the spectral reflectance can be reconstructed through mathematical model. When we get the digital counts of testing object by a snapshot, we are able to obtain the spectral reflectance. The experimental result shows the color difference is smaller than 3, which means the mathematical model is robust and savvy. Finally, we applied this technology to distinguish the phenomenon of Metamerism. We expect this technology can be applied inspection of bio-medical engineering in the future.

參考文獻


[2] Shoji Tominaga et al., JOSA A Vol. 29, p.1764-1775 (2012).
[5] S. Tominaga, “Spectral imaging by a multi-channel camera,” J. Electron. Imaging 8, 332–341 (1999).
[6] M. A. López-Álvarez, J. Hernández-Andrés, and J. Romero, “Developing an optimum computer-designed multispectral system comprising a monochrome CCD camera and a liquid-crystal tunable filter,” Appl. Opt. 47, 4381–4390 (2008).
[7] M. A. López-Álvarez, J. Hernández-Andrés, J. Romero, J. Campos, and A. Pons, “Calibrating the elements of a multispectral imaging system,” J. Imaging Sci. Technol. 53, 031102 (2009).
[11] I. T. Jolliffe, Principal component analysis, New York : Springer.(2002)

延伸閱讀