A Moon image point spread function deconvolution framework for satellite Earth observation image restoration

Accurate restoration of remote sensing imagery is challenged by complex degradation factors, including atmospheric turbulence and sensor imperfections. A major bottleneck lies in the difficulty of disentangling atmospheric effects from the system’s intrinsic Point Spread Function (PSF), which limits the stability and generalization of conventional restoration methods. This study proposes MoonPSF-Decon, which derives an intrinsic, sensor-consistent PSF from high-contrast, atmosphere-free FY-4B/AGRI lunar observations and applies it to Earth imagery for non-blind deconvolution using Wiener (WN) and Lucy – Richardson (LR) deconvolution algorithms. Unlike prior lunar-target studies focused on Modulation Transfer Function (MTF) diagnostics, we demonstrate short-term PSF reusability: a single MoonPSF extracted on 28 September 2023 is reused to restore a month-long sequence of AGRI Earth images (1–31 October 2023), improving operational efficiency. A progressive validation strategy – including simulation, real-scene restoration, and cross-sensor comparison with near-synchronous Himawari-9/AHI high-resolution data – confirms the method’s effectiveness. For AGRI 10.8 µm imagery, peak signal-to-noise ratio (PSNR) increases by 1.8% for both LR and WN, root mean square error (RMSE) decreases by 8.1% (LR) and 8.0% (WN), and structural similarity (SSIM) improves by 0.75% (LR) and 0.8% (WN). The month-long evaluation shows consistently improved PSNR/SSIM and reduced RMSE relative to the AHI quasi-reference, supporting the temporal robustness of the lunar-derived intrinsic PSF. Overall, MoonPSF-Decon provides a stable and physically grounded PSF prior for practical, multi-temporal Earth observation image deblurring.