The accuracy of spot centroid positioning has a significant impact on the tracking accuracy of the system and the stability of the laser link construction.In satellite laser communication systems,the use of short-wave...The accuracy of spot centroid positioning has a significant impact on the tracking accuracy of the system and the stability of the laser link construction.In satellite laser communication systems,the use of short-wave infrared wavelengths as beacon light can reduce atmospheric absorption and signal attenuation.However,there are strong non-uniformity and blind pixels in the short-wave infrared image,which makes the image distorted and leads to the decrease of spot centroid positioning accuracy.Therefore,the high-precision localization of the spot centroid of the short-wave infrared images is of great research significance.A high-precision spot centroid positioning model for short-wave infrared is proposed to correct for non-uniformity and blind pixels in short-wave infrared images and quantify the localization errors caused by the two,further model-based localization error simulations are performed,and a novel spot centroid positioning payload for satellite laser communications has been designed using the latest 640×512 planar array InGaAs shortwave infrared detector.The experimental results show that the non-uniformity of the corrected image is reduced from 7%to 0.6%,the blind pixels rejection rate reaches 100%,the frame rate can be up to 2000 Hz,and the spot centroid localization accuracy is as high as 0.1 pixel point,which realizes high-precision spot centroid localization of high-frame-frequency short-wave infrared images.展开更多
The encoding aperture snapshot spectral imaging system,based on the compressive sensing theory,can be regarded as an encoder,which can efficiently obtain compressed two-dimensional spectral data and then decode it int...The encoding aperture snapshot spectral imaging system,based on the compressive sensing theory,can be regarded as an encoder,which can efficiently obtain compressed two-dimensional spectral data and then decode it into three-dimensional spectral data through deep neural networks.However,training the deep neural net⁃works requires a large amount of clean data that is difficult to obtain.To address the problem of insufficient training data for deep neural networks,a self-supervised hyperspectral denoising neural network based on neighbor⁃hood sampling is proposed.This network is integrated into a deep plug-and-play framework to achieve self-supervised spectral reconstruction.The study also examines the impact of different noise degradation models on the fi⁃nal reconstruction quality.Experimental results demonstrate that the self-supervised learning method enhances the average peak signal-to-noise ratio by 1.18 dB and improves the structural similarity by 0.009 compared with the supervised learning method.Additionally,it achieves better visual reconstruction results.展开更多
基金Supported by the Short-wave Infrared Camera Systems(B025F40622024)。
文摘The accuracy of spot centroid positioning has a significant impact on the tracking accuracy of the system and the stability of the laser link construction.In satellite laser communication systems,the use of short-wave infrared wavelengths as beacon light can reduce atmospheric absorption and signal attenuation.However,there are strong non-uniformity and blind pixels in the short-wave infrared image,which makes the image distorted and leads to the decrease of spot centroid positioning accuracy.Therefore,the high-precision localization of the spot centroid of the short-wave infrared images is of great research significance.A high-precision spot centroid positioning model for short-wave infrared is proposed to correct for non-uniformity and blind pixels in short-wave infrared images and quantify the localization errors caused by the two,further model-based localization error simulations are performed,and a novel spot centroid positioning payload for satellite laser communications has been designed using the latest 640×512 planar array InGaAs shortwave infrared detector.The experimental results show that the non-uniformity of the corrected image is reduced from 7%to 0.6%,the blind pixels rejection rate reaches 100%,the frame rate can be up to 2000 Hz,and the spot centroid localization accuracy is as high as 0.1 pixel point,which realizes high-precision spot centroid localization of high-frame-frequency short-wave infrared images.
基金Supported by the Zhejiang Provincial"Jianbing"and"Lingyan"R&D Programs(2023C03012,2024C01126)。
文摘The encoding aperture snapshot spectral imaging system,based on the compressive sensing theory,can be regarded as an encoder,which can efficiently obtain compressed two-dimensional spectral data and then decode it into three-dimensional spectral data through deep neural networks.However,training the deep neural net⁃works requires a large amount of clean data that is difficult to obtain.To address the problem of insufficient training data for deep neural networks,a self-supervised hyperspectral denoising neural network based on neighbor⁃hood sampling is proposed.This network is integrated into a deep plug-and-play framework to achieve self-supervised spectral reconstruction.The study also examines the impact of different noise degradation models on the fi⁃nal reconstruction quality.Experimental results demonstrate that the self-supervised learning method enhances the average peak signal-to-noise ratio by 1.18 dB and improves the structural similarity by 0.009 compared with the supervised learning method.Additionally,it achieves better visual reconstruction results.
