Remote Sensing Image Restoration Method Based on Lorentz Fitting Point Spread Function

Aiming at the problem that the existing remote sensing image restoration effect is poor due to the mismatch between the point spread function (PSF) model and the actual blur kernel, an image restoration method based on a Lorentz fitted PSF is proposed to fully fit the estimated PSF of a remote sensing image and to improve the restoration accuracy. Firstly, considering the matching error between the existing model and the actual degradation process, the blurred kernel is modeled as a linear combination of basic two-dimensional models, and a Lorentz function is used as the basis function to model the blurred effect caused by actual degradation. Then, while selecting the ground object with edge characteristics to estimate the degradation function of the remote sensing imaging system, the established mathematical model is used for fitting correction of the estimated point spread function. After fitting correction, the point spread function is applied to the remote sensing image restoration and to reduce the interference of the degradation ambiguity of the imaging system. Finally, the corrected point spread function and the Richardson-Lucy restoration algorithm are used to restore the remote sensing image. The experimental results show that compared with those of the existing remote sensing image restoration methods based on other point spread function models, the effectiveness of the proposed method is significantly enhanced.