本文介绍了红外感知技术在“向上看”的深空探测与“向下看”的对地观测两大领域的发展脉络和前沿挑战。在深空探测方面,以詹姆斯·韦布空间望远镜(James Webb Space Telescope, JWST)为代表,该技术正通过超大口径、甚长波段和超低...本文介绍了红外感知技术在“向上看”的深空探测与“向下看”的对地观测两大领域的发展脉络和前沿挑战。在深空探测方面,以詹姆斯·韦布空间望远镜(James Webb Space Telescope, JWST)为代表,该技术正通过超大口径、甚长波段和超低温制冷等手段,不断逼近观测的物理极限,并致力于揭示宇宙早期的历史奥秘。阐述了对地观测领域不同时期搭载于各类卫星的红外探测载荷的主要发展过程及其代表性和特色。红外对地观测技术从过去宽幅下的低空间分辨率和少量波段,向着空谱大范围下的时-空-谱-辐分辨率提升迈进。大口径低背景光学、长波长高灵敏度探测器、片上智能感知、大数据孪生系统等新型技术,以及通导遥一体的“即时遥感”和商业航天的大规模发展,将助力构建“红外数字地球”底座,增强人类对地球多圈层异常事件以及各种复杂循环过程演变的实时认知和精准预测,推动红外对地观测技术走向大众化应用。展开更多
The Savitzky-Golay(SG)filter,which employs polynomial least-squares approximations to smooth data and estimate derivatives,is widely used for processing noisy data.However,noise suppression by the SG filter is recogni...The Savitzky-Golay(SG)filter,which employs polynomial least-squares approximations to smooth data and estimate derivatives,is widely used for processing noisy data.However,noise suppression by the SG filter is recognized to be limited at data boundaries and high frequencies,which can significantly reduce the signal-to-noise ratio(SNR).To solve this problem,a novel method synergistically integrating Principal Component Analysis(PCA)with SG filtering is proposed in this paper.This approach avoids the is-sue of excessive smoothing associated with larger window sizes.The proposed PCA-SG filtering algorithm was applied to a CO gas sensing system based on Cavity Ring-Down Spectroscopy(CRDS).The perform-ance of the PCA-SG filtering algorithm is demonstrated through comparison with Moving Average Filtering(MAF),Wavelet Transformation(WT),Kalman Filtering(KF),and the SG filter.The results demonstrate that the proposed algorithm exhibits superior noise reduction capabilities compared to the other algorithms evaluated.The SNR of the ring-down signal was improved from 11.8612 dB to 29.0913 dB,and the stand-ard deviation of the extracted ring-down time constant was reduced from 0.037μs to 0.018μs.These results confirm that the proposed PCA-SG filtering algorithm effectively improves the smoothness of the ring-down curve data,demonstrating its feasibility.展开更多
太赫兹成像技术虽已被证实能够用于检测葵花籽内部品质,然而其成像速度较为缓慢,难以实现切实且迅速的检测。为了实现对葵花籽饱满度的快速检测,该研究将压缩感知与注意力增强超分辨率生成对抗网络(A-ESRGAN)模型相结合应用于太赫兹成...太赫兹成像技术虽已被证实能够用于检测葵花籽内部品质,然而其成像速度较为缓慢,难以实现切实且迅速的检测。为了实现对葵花籽饱满度的快速检测,该研究将压缩感知与注意力增强超分辨率生成对抗网络(A-ESRGAN)模型相结合应用于太赫兹成像领域。首先,选用压缩采样匹配追踪(compressive sampling matching pursuit,CoSaMP)重构算法来验证不同测量矩阵的性能,根据最佳综合性能选取高斯矩阵作为测量矩阵。其次,通过比较基于交替方向乘子法(alternating direction method of multipliers,ADMM)结合全变分(total variation,TV)正则化(ADMM_TV)和子空间追踪(subspace pursuit,SP)等5种重构算法的峰值信噪比和重构时间等评价指标评估图像重建质量。结果表明ADMM_TV在峰值信噪比、均方误差、结构相似性指数表现最佳,自然图像质量评估器在测量比例超过6.0%最低,尽管重构时间无明显优势,但综合表现优于其他算法。最后,运用多尺度注意力增强超分辨率生成对抗网络(A-ESRGANmulti)模型对压缩感知不同采样率的重构图像进行处理,其效果优于真实图像增强超分辨率生成对抗网络(RealESRGAN)和单尺度注意力增强超分辨率生成对抗网络(A-ESRGAN-single),提升了图像质量,使边缘对比度得以提高,为后续的图像分割提供了便利。研究表明,压缩感知与A-ESRGAN-multi模型相结合用于检测葵花籽饱满度是可行的,验证集的饱满度误差平均为2.50%,最大检测误差为6.41%。综上所述,将压缩感知与A-ESRGAN-multi模型相结合,能够有效地节省82.5%的采样时间,为葵花籽的品质检测开辟了新的途径。展开更多
文摘本文介绍了红外感知技术在“向上看”的深空探测与“向下看”的对地观测两大领域的发展脉络和前沿挑战。在深空探测方面,以詹姆斯·韦布空间望远镜(James Webb Space Telescope, JWST)为代表,该技术正通过超大口径、甚长波段和超低温制冷等手段,不断逼近观测的物理极限,并致力于揭示宇宙早期的历史奥秘。阐述了对地观测领域不同时期搭载于各类卫星的红外探测载荷的主要发展过程及其代表性和特色。红外对地观测技术从过去宽幅下的低空间分辨率和少量波段,向着空谱大范围下的时-空-谱-辐分辨率提升迈进。大口径低背景光学、长波长高灵敏度探测器、片上智能感知、大数据孪生系统等新型技术,以及通导遥一体的“即时遥感”和商业航天的大规模发展,将助力构建“红外数字地球”底座,增强人类对地球多圈层异常事件以及各种复杂循环过程演变的实时认知和精准预测,推动红外对地观测技术走向大众化应用。
文摘The Savitzky-Golay(SG)filter,which employs polynomial least-squares approximations to smooth data and estimate derivatives,is widely used for processing noisy data.However,noise suppression by the SG filter is recognized to be limited at data boundaries and high frequencies,which can significantly reduce the signal-to-noise ratio(SNR).To solve this problem,a novel method synergistically integrating Principal Component Analysis(PCA)with SG filtering is proposed in this paper.This approach avoids the is-sue of excessive smoothing associated with larger window sizes.The proposed PCA-SG filtering algorithm was applied to a CO gas sensing system based on Cavity Ring-Down Spectroscopy(CRDS).The perform-ance of the PCA-SG filtering algorithm is demonstrated through comparison with Moving Average Filtering(MAF),Wavelet Transformation(WT),Kalman Filtering(KF),and the SG filter.The results demonstrate that the proposed algorithm exhibits superior noise reduction capabilities compared to the other algorithms evaluated.The SNR of the ring-down signal was improved from 11.8612 dB to 29.0913 dB,and the stand-ard deviation of the extracted ring-down time constant was reduced from 0.037μs to 0.018μs.These results confirm that the proposed PCA-SG filtering algorithm effectively improves the smoothness of the ring-down curve data,demonstrating its feasibility.
文摘太赫兹成像技术虽已被证实能够用于检测葵花籽内部品质,然而其成像速度较为缓慢,难以实现切实且迅速的检测。为了实现对葵花籽饱满度的快速检测,该研究将压缩感知与注意力增强超分辨率生成对抗网络(A-ESRGAN)模型相结合应用于太赫兹成像领域。首先,选用压缩采样匹配追踪(compressive sampling matching pursuit,CoSaMP)重构算法来验证不同测量矩阵的性能,根据最佳综合性能选取高斯矩阵作为测量矩阵。其次,通过比较基于交替方向乘子法(alternating direction method of multipliers,ADMM)结合全变分(total variation,TV)正则化(ADMM_TV)和子空间追踪(subspace pursuit,SP)等5种重构算法的峰值信噪比和重构时间等评价指标评估图像重建质量。结果表明ADMM_TV在峰值信噪比、均方误差、结构相似性指数表现最佳,自然图像质量评估器在测量比例超过6.0%最低,尽管重构时间无明显优势,但综合表现优于其他算法。最后,运用多尺度注意力增强超分辨率生成对抗网络(A-ESRGANmulti)模型对压缩感知不同采样率的重构图像进行处理,其效果优于真实图像增强超分辨率生成对抗网络(RealESRGAN)和单尺度注意力增强超分辨率生成对抗网络(A-ESRGAN-single),提升了图像质量,使边缘对比度得以提高,为后续的图像分割提供了便利。研究表明,压缩感知与A-ESRGAN-multi模型相结合用于检测葵花籽饱满度是可行的,验证集的饱满度误差平均为2.50%,最大检测误差为6.41%。综上所述,将压缩感知与A-ESRGAN-multi模型相结合,能够有效地节省82.5%的采样时间,为葵花籽的品质检测开辟了新的途径。
