Using deep learning for effective simulation of ghost reflections

Clermont, L.; Adam, G.

doi:10.1016/j.rio.2024.100643

Cited by 2 publications

References 27 publications

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A model for suppressing stray light in astronomical images based on deep learning

Chen,

Zhao,

Yang

et al. 2024

Sci Rep

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Wide-Field Small Aperture Telescopes (WFSAT) are widely used for surveilling space objects. Due to their wide-field of view (FOV) characteristics, these telescopes can cover a large areas of the sky at once, improving observation efficiency. However, a wide-field optical telescope is highly sensitive to external stray light (such as moonlight and thin clouds), which can significantly reduce the quality of observation data. In severe cases, it can cause the telescope to malfunction and inaccurately position the object. In response to this problem, this paper proposes a model for suppressing stray light in astronomical images based on deep learning: the Pyramid Deformable Large Kernel Attention (PD-LKA) Model. This model expands the receptive field through a pyramid structure, captures multi-scale features, and improves the model’s robustness to various scales of stray light interference. Meanwhile, through the Deformable Large Kernel Attention (D-LKA), the model can more accurately locate and enhance the feature extraction ability in areas affected by stray light interference, thereby better suppressing stray light.Using simulated astronomical image pairs to train the model, the tests achieved a PSNR of up to 32.540 and an SSIM of up to 0.938. Finally, the model is applied to a image sequence with real stray light interference. The restored images undergo astronomical positioning and orbital association processing. The results show that the positioning accuracy of the object is better than 5 arcseconds. This indicates that the model proposed in this paper not only recovers the object and background stars but also effectively preserves their gray values, shapes, and positional information.

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A model for suppressing stray light in astronomical images based on deep learning

Chen,

Zhao,

Yang

et al. 2024

Sci Rep

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Using deep learning to improve stray light optical simulations in space telescopes

Clermont,

Adam

2024

Modeling, Systems Engineering, and Project Management for Astronomy XI

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Stray light (SL) control is an important aspect in the development of optical instruments. Iterations are necessary between design and analysis phases, where ray tracing simulations are performed for performance prediction. This process involves trial and error, requiring to be able to perform rapid evaluation of SL properties. The limitation is that accurate SL simulations require sending many rays, which can be time consuming. In this paper, we use deep learning to improve the accuracy of SL maps even when obtained with very few rays. Two different deep learning methods are used.The training process is performed by generating a large database of artificial SL maps, with different noise levels reproduced with a Poisson distribution. Once the training completed, we show that the autoencoder performs the best and improves significantly the accuracy of SL maps. Even with extremely small number of rays, it recovers complex SL patterns which are not visible on raw ray traced maps. This method thus enables more efficient iterations between design and analysis. It is also useful for developing SL correction algorithms, as it requires tracing SL maps under large number of illumination conditions in a reasonable amount of time.

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Using deep learning for effective simulation of ghost reflections

Cited by 2 publications

References 27 publications

A model for suppressing stray light in astronomical images based on deep learning

A model for suppressing stray light in astronomical images based on deep learning

Using deep learning to improve stray light optical simulations in space telescopes

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