光譜訊號的特徵峰值是光譜訊號分析中重要資訊之一，在人為操作的情況下特徵峰值的位置簡單地看出來並選取出粗略的位置，然而要自動化找出精確峰值位置就會因為光譜本身受到雜訊以及基線的影響導致實行的困難。本研究建立了一個演算方法能夠將線光譜的峰值偵測達到自動化，在流程中針對雜訊以及基線皆有對應的處理。對於雜訊採用Savitzky-Golay平滑濾波器將特徵保留的同時去除雜訊，基線的校正使用AWFPSI演算法來進行自動化處理。雜訊與基線的處理結束後利用希爾伯特轉換將每個峰值分割出來再個別透過高斯擬合來得到精確的峰值位置。利用模擬訊號進行演算法測試得到峰值位置誤差百分比0.1%以下的結果。接著再以實際的光譜訊號進行測試應用於波長校正流程，光譜訊號包含光譜儀量測之訊號以及光譜影像，在實驗最後得到本研究自動化偵測到的峰值位置波長校正後與實際波長的誤差很小的結果。

Peak is one of the important information in spectral analysis. It is easy to find peak location roughly by people, however, to automatically find the accurate peak location is very difficult because of the effect from noise and baseline. In this study we develop an automated algorithm of line spectrum peak detection and we have the processes for both noise and baseline in the procedure. We use the Savitzky-Golay filter to smoothing the signal and use the AWFPSI algorithm to do the baseline correction. After smoothing and baseline correction, we use the Hilbert transform to separate peaks and then do the Gaussian fitting for each peak to get the accurate peak location. About tests, first we use the algorithm in simulated signal, and get the result that the error is under 0.1%. And then, we apply the algorithm to experimental signals of both data from spectrometer and spectral images and do the wavelength correction. The results show that the error between the transformed wavelength and the real wavelength is very small.

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