ing at the problem of locking the long-term frequency drift of a laser, a digital laser frequency stabilization system is achieved with reference to an optical frequency comb. In this system, the heterodyne interference between the slave laser and the optical frequency comb is first performed to obtain the beat signal which represents the laser frequency drift. Then, the frequency of the beat signal is measured by a self-developed digital-counting-based frequency to voltage conversion circuit, and the value of the measured frequency is converted to an error voltage signal. At last, the slave laser is controlled through the feedback control of the master program. In the long-term frequency stabilization experiment of a 760 nm narrow linewidth semiconductor laser, the system improves the long-term stability of the laser frequency by two orders of magnitude, and the stability of the laser reaches 4.4×10 ^-10(τ=262 s). Experimental result shows that the proposed system can achieve a long-term frequency drift locking for lasers with wavelength locating in the spectrum range of the optical frequency comb. This system can provide a basis for further fine-locking of the frequency and phase of the laser.

ing 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.