Methods, apparatus, electronic devices, and storage media for reconstructing single-frame astronomical images

By employing a blind deconvolution algorithm based on a fully variational prior of a reweighted graph and iterative optimization, the problem of image quality degradation from ground-based optical telescopes was solved, achieving efficient deblurring of single-frame astronomical images and obtaining high-resolution images.

CN118967513BActive Publication Date: 2026-06-30TSINGHUA UNIVERSITY
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
TSINGHUA UNIVERSITY
Filing Date
2024-08-19
Publication Date
2026-06-30

AI Technical Summary

Technical Problem

Astronomical images acquired by ground-based optical telescopes are affected by atmospheric turbulence, resulting in a decline in image quality. Existing technologies rely on multi-frame image processing, which is computationally expensive and makes it difficult to effectively reconstruct high-resolution astronomical images.

Method used

A blind deconvolution algorithm based on reweighted graph total variation prior is adopted. Multiple skeleton images are generated by resampling. Combined with iterative optimization and light intensity correction, the target blur kernel is determined and the single-frame astronomical image is deblurred.

Benefits of technology

Without requiring additional data support, image processing efficiency was improved, computational resources and time costs were reduced, and high-resolution and high-quality deblurred single-frame astronomical images were obtained.

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Abstract

This disclosure relates to a method, apparatus, electronic device, and storage medium for reconstructing a single-frame astronomical image, comprising: determining a blind deconvolution optimization model corresponding to an initial single-frame astronomical image using a blind deconvolution algorithm based on a reweighted graph total variational prior; resampling the initial single-frame astronomical image to determine n skeleton images corresponding to the initial single-frame astronomical image; performing iterative optimization and intensity correction processing based on the blind deconvolution optimization model and the n skeleton images to determine a target blur kernel corresponding to the initial single-frame astronomical image; and deblurring the initial single-frame astronomical image based on the target blur kernel to determine a deblurred single-frame astronomical image. This disclosure can utilize a blind deconvolution algorithm based on an RGTV prior to iteratively optimize and correct the intensity of the initial single-frame astronomical image, obtaining a high-quality deblurred single-frame astronomical image with less computational resources and time required, thereby reducing image reconstruction costs and improving processing efficiency.
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