Enhanced Criminisi Algorithm with P-Laplace Operator and PSNR Evaluation

In this paper, we present a comprehensive enhancement of the Criminisi algorithm to improve both efficiency and quality in image inpainting. Our implementation replaces the standard data term with a P-Laplace operator, which better preserves edge information through its adaptive diffusion properties. The priority calculation module now incorporates this operator using a weighted gradient computation that emphasizes structural continuity. For the actual inpainting process, we employ advanced interpolation algorithms with gradient-aware filling mechanisms that prioritize texture synthesis along isophote directions. Additionally, we've integrated a PSNR calculation function that automatically compares the inpainted region with the original undamaged areas, providing quantitative quality metrics through mean squared error computation and logarithmic scaling. These improvements collectively enhance the algorithm's accuracy in target pixel selection and reconstruction fidelity, contributing significant advancements to the field of image processing.