Improving image quality of the Solar Disk Imager (SDI) of the Lyman-alpha Solar Telescope (LST) onboard the ASO-S mission
Hui Liu, Hui Li, Sizhong Zou, Kaifan Ji, Zhenyu Jin, Jiahui Shan,, Jingwei Li, Guanglu Shi, Yu Huang, Li Feng, Jianchao Xue, Qiao Li, Dechao, Song, Ying Li

TL;DR
This paper introduces a novel deep learning-based algorithm, SPIBOA, that estimates the PSF of the SDI instrument onboard ASO-S, significantly improving image resolution and reducing noise in solar observations.
Contribution
We developed the SPIBOA algorithm combining deep learning and optical modeling to enhance SDI image quality without prior PSF knowledge.
Findings
Image resolution increased by over three times after correction
Significant noise reduction in SDI images
SPIBOA integrated into routine data processing
Abstract
The in-flight calibration and performance of the Solar Disk Imager (SDI), which is a pivotal instrument of the Lyman-alpha Solar Telescope (LST) onboard the Advanced Space-based Solar Observatory (ASO-S) mission, suggested a much lower spatial resolution than expected. In this paper, we developed the SDI point-spread function (PSF) and Image Bivariate Optimization Algorithm (SPIBOA) to improve the quality of SDI images. The bivariate optimization method smartly combines deep learning with optical system modeling. Despite the lack of information about the real image taken by SDI and the optical system function, this algorithm effectively estimates the PSF of the SDI imaging system directly from a large sample of observational data. We use the estimated PSF to conduct deconvolution correction to observed SDI images, and the resulting images show that the spatial resolution after…
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