Blind deblurring of astronomical images using a SCGTV-based single-frame method

Atmospheric turbulence often distorts space target imaging, leading to degraded image quality. To address the issue of image quality degradation, various hardware and software approaches have been proposed, including adaptive optics, lucky imaging, and blind deconvolution. Traditional astronomical image deblurring algorithms rely on information from multiple frames, requiring extensive processing time and computational resources. This study introduces the saturation-corrected graph total variation (SCGTV) method to address low signal-to-noise ratio and pixel saturation in single-frame astronomical image deblurring. This method effectively minimizes the influence of atmospheric turbulence and noise by leveraging a reweighted graph total variation prior. Saturation correction and dark channel information are integrated in SCGTV to enhance resolution and reduce artifacts. The SCGTV method is well-designed for astronomical scenarios, successfully deblurring real-world astronomical images and demonstrating superior performance on thirty simulated dataset compared to other algorithms. Quantitative evaluations on artificially blurred datasets demonstrate that SCGTV outperforms existing methods. These enhancements provide significant benefits for astronomical observation and analysis.