Remote sensing image fine-processing method based on the adaptive hyper-Laplacian prior

High-quality remote sensing images have wide application prospects in agroforestry investigation, target monitoring, disaster prevention, urban planning and military defense. However, remote sensing imaging links such as atmosphere, platform and optical system seriously affect the ability of image interpretation and analysis. The traditional regularized processing methods have a strong ability to improve the definition, but most of them may sacrifice texture details or introduce artifacts, because their fixed prior parameters cannot fully adapt to various kinds of scenes. To address this problem, we propose a novel fine-processing method based on the adaptive hyper-Laplacian prior for remote sensing imaging systems. The method is developed by automatically updating and optimizing the prior parameters and objective function in the iterative process based on the prior characteristics of different regions of remote sensing images. Experimentally, the proposed method can realize the fine-processing of remote sensing images, including the edge enhancement, texture detail preservation, and artifact suppression.

Automated summary

High-quality remote sensing images have wide application prospects, but traditional processing methods may sacrifice details or introduce artifacts. The novel fine-processing method based on the adaptive hyper-Laplacian prior can automatically update and optimize parameters to adapt to different scenes. Experimental results show that the method can achieve fine-processing of remote sensing images, including edge enhancement, texture detail preservation, and artifact suppression.