Deep-Dark-Net:一种基于生成对抗网络的导星相机暗流预测模型OA北大核心CSTPCD

The dark current affects image quality,reduces the signal-to-noise ratio of star images,and impacts the accuracy of star position and flux measurements.Therefore,es-timating and eliminating dark currents accurately is crucial when processing astronomical data.To meet the demands of LAMOST guide star image processing,this paper proposes a novel method for processing historical guide star images with high precision in the absence of dark field images,simplifying the capture process of dark field images using guide star cameras.Utilizing the characteristics of LAMOST guide star raw data,we introduce a new approach based on a generative adversarial network model,Deep-Dark-Net,to precisely es-timate dark current.This method employs a conditional generative adversarial network to construct a correlation model between the Overscan and Optical Black areas of guide star images and their corresponding imaging areas.This model allows inversion and reconstruc-tion of high-precision dark field images through these areas.Experiments demonstrate that the dark current predicted by Deep-Dark-Net aligns more closely with actual dark current than traditional methods,fulfilling the requirements for dark field image processing in LAM-OST telescope guide star imagery.This work offers a new approach and methodology for handling astronomical image dark current.It highlights deep learning technology's potential value and application direction in astronomical image processing.