Edge-Preserving Image Smoothing Based on Local Structure Reconstruction

Abstract

Edge-preserving filters serve as a fundamental component in computational photography and computer vision. Traditional filtering methods are generally classified into local and global approaches; however, the lack of full integration between the two often leads to the degradation of weak structural information. To address this issue, we propose an edge-preserving image smoothing based on local structure reconstruction. The proposed algorithm integrates a global optimization strategy while fully leveraging the intrinsic correlation between neighboring pixels, thereby significantly enhancing both smoothing quality and edge preservation. Our method unifies the $L_p(0<p\le 2)$ model framework, enabling diverse smoothing effects by adjusting the parameter $p$. Compared to existing edge-preserving filters, the proposed approach demonstrates superior performance in both visual quality and quantitative evaluation metrics.