Edge-aware adaptive partial differential equation image inpainting for high-quality restoration

Abstract

Image inpainting aims to reconstruct missing or damaged regions in digital images while preserving structural consistency and visual coherence. Among partial differential equation (PDE)-based approaches, the model introduced by Marcelo Bertalmio is a foundational technique for isophote-driven structure propagation. However, its performance often degrades in large damaged re gions. This limitation is mainly caused by insufficient transport strength and isotropic diffusion, which lead to structural blurring. In this paper, we propose an edge-aware adaptive PDE inpainting model that introduces a transport am plification factor and a spatially-adaptive diffusion term to improve edge prop agation and structural preservation. Experimental results on various test im ages demonstrate that the proposed approach improves structural reconstruction. Quantitative evaluation using peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) metrics confirms higher restoration accuracy and better edge preservation compared with baseline methods.