Super-resolution Reconstruction Algorithm for Depth Image Based on Fractional Calculus

This study presents a new method for super-resolution of depth images by combining fractional calculus and inverse distance interpolation. The method improves edge recognition through fractional differential and solves the problem of missing data in traditional super-resolution algorithms for depth images. Results show that the proposed method outperforms classical interpolation algorithms, with improved PSNR by 3-10dB. The method effectively solves the impact of depth image edge distance on interpolation results, and provides more texture information.