中阶梯光栅光谱仪谱图还原技术发展与展望（特邀）
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基于全像面拟合的中阶梯光栅光谱仪谱图还原算法
Objective The echelle spectrometer, with its high spectral resolution, is increasingly applied in various fields and has become one of the primary spectroscopic analysis instruments. Spectrum reconstruction technology is at the core of data processing in echelle spectrometers. It achieves rapid reconstruction from twodimensional (2D) images to onedimensional (1D) spectra by establishing a correspondence between the wavelength and imaging position. The accuracy of the spectrum reconstruction directly determines the performance of the echelle spectrometer, making it a key and challenging aspect of instrument development. Spectrum reconstruction algorithms have evolved from ray tracing, modeling (deviation method and mathematical modeling), and calibration methods. The evolution of algorithms is an ongoing process of continuous optimization and improvement. Each spectrum reconstruction algorithm has its advantages and disadvantages. However, a consistent mainstream approach is to achieve high accuracy and speed. Factors such as environmental conditions and application requirements must also be considered. Therefore, it is crucial to develop a spectrum reconstruction algorithm that combines these various advantages.Methods This study proposes a convenient and widely applicable spectrum reconstruction algorithm , adopting a nontraditional approach that initially focuses on improving the modeling speed, followed by further enhancement of accuracy. The main research method involves leveraging the advantage of rapid modeling using the deviation method to establish an initial model quickly.Subsequently, the initial model is subjected to holographic surface fitting with the theoretical model traced using raytracing software to obtain a standard model. Calibration is thereafter incorporated into the modeling process, allowing the standard model to fit an actual model comprising elemental lamp spectrum data. Through this process, the final model is obtained, and a spectrum reconstruction model is established. Following this, denoising is applied to the 2D spectra of the elemental lamps, completing the wavelength extraction. Finally, five elemental lamps are selected as test light sources to validate the accuracy of the proposed algorithm.Results and Discussions Holographic surface fitting is performed between the initial and theoretical models (Fig. 7). After holographic surface fitting, a standard model is obtained (Fig. 8). The error within the holographic surface of the standard model is within 2 pixel (Table 3). In the twostage modeling process, the standard model is fitted with the actual model to obtain the final 0811003 -14 研究论文 第 51 卷 第 8 期/2024 年 4 月/中国激光 model. The error within the holographic surface of the final model after fitting is within 3 pixel (Fig. 10). In the image denoising process, a denoising algorithm is developed based on the characteristics of the original 2D spectrum, accomplishing the denoising task and removing the majority of the noise (Fig. 13). Finally, by selecting five types of elemental lam...
基于全像面拟合的中阶梯光栅光谱仪谱图还原算法
Objective The echelle spectrometer, with its high spectral resolution, is increasingly applied in various fields and has become one of the primary spectroscopic analysis instruments. Spectrum reconstruction technology is at the core of data processing in echelle spectrometers. It achieves rapid reconstruction from twodimensional (2D) images to onedimensional (1D) spectra by establishing a correspondence between the wavelength and imaging position. The accuracy of the spectrum reconstruction directly determines the performance of the echelle spectrometer, making it a key and challenging aspect of instrument development. Spectrum reconstruction algorithms have evolved from ray tracing, modeling (deviation method and mathematical modeling), and calibration methods. The evolution of algorithms is an ongoing process of continuous optimization and improvement. Each spectrum reconstruction algorithm has its advantages and disadvantages. However, a consistent mainstream approach is to achieve high accuracy and speed. Factors such as environmental conditions and application requirements must also be considered. Therefore, it is crucial to develop a spectrum reconstruction algorithm that combines these various advantages.Methods This study proposes a convenient and widely applicable spectrum reconstruction algorithm , adopting a nontraditional approach that initially focuses on improving the modeling speed, followed by further enhancement of accuracy. The main research method involves leveraging the advantage of rapid modeling using the deviation method to establish an initial model quickly.Subsequently, the initial model is subjected to holographic surface fitting with the theoretical model traced using raytracing software to obtain a standard model. Calibration is thereafter incorporated into the modeling process, allowing the standard model to fit an actual model comprising elemental lamp spectrum data. Through this process, the final model is obtained, and a spectrum reconstruction model is established. Following this, denoising is applied to the 2D spectra of the elemental lamps, completing the wavelength extraction. Finally, five elemental lamps are selected as test light sources to validate the accuracy of the proposed algorithm.Results and Discussions Holographic surface fitting is performed between the initial and theoretical models (Fig. 7). After holographic surface fitting, a standard model is obtained (Fig. 8). The error within the holographic surface of the standard model is within 2 pixel (Table 3). In the twostage modeling process, the standard model is fitted with the actual model to obtain the final 0811003 -14 研究论文 第 51 卷 第 8 期/2024 年 4 月/中国激光 model. The error within the holographic surface of the final model after fitting is within 3 pixel (Fig. 10). In the image denoising process, a denoising algorithm is developed based on the characteristics of the original 2D spectrum, accomplishing the denoising task and removing the majority of the noise (Fig. 13). Finally, by selecting five types of elemental lam...
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