基于层次结构引导LoG滤波的红外星图去噪算法OA

To address the issue of low signal-to-noise ratio(SNR)in star images caused by time-correlated noise introduced by the detector and external environment,this paper proposes a infrared star map denoising algorithm based on hierarchical structure-guided Laplacian of Gaussian(LoG)filtering.Single-frame infrared star images are takenas the processing objects,and a three-layer rectangular region comprising the target,transition,and background areas is constructed.By integrating the standard LoG operator for response weighting,the algorithm enhances the star targets while suppressing the background noise.The star points are extracted from the diffusion-based connected domain.The experimental validation on real ground-based infrared star images demonstrates that the proposed method effectively suppresses noise while preserving the structural integrity of star points.Compared with traditional denoising algorithms,the proposed approach achieves an 18.8%improvement in the image SNR and an 80.6%increase in the background suppression factor(BSF).The algorithm is computationally efficient for single-frame processing,making it well-suited for the deployment on embedded platforms and applicable to star detection and real-time astronomical observation tasks.It offers strong engineering practicality and significant potential for broader application.