Super-Resolution Virtual Scene Rendering Technology Based on Generalized Huber-MRF Image Modeling

Abstract

Abstract The traditional rendering technology creates virtual scenes with insufficient fidelity, which are quite different from real scenes. To address this issue, a super-resolution virtual scene rendering technology based on generalized Huber-MRF image modeling has been studied. This study preprocesses the original image through three steps: graying, filtering, and enhancement. The generalized Huber-MRF is employed for super-resolution image restoration to enhance image clarity. Corner features are extracted from the super-resolution image, and the Delaunay triangular grid method is used to construct the image's 3D model. Texture and lighting conditions of the virtual scene are then set through texture mapping, shadow rendering, and other technologies to achieve realistic scene effects. The results indicate that, when applied, the research technology yields a relatively small chamfer distance in virtual scene modeling, suggesting that the design method preserves the details and shape information of the original image, reducing the difference between the virtual scene and the real scene and increasing the fidelity of the virtual scene. Furthermore, this method achieves maximum PSNR and SSIM values of 17.54 and 0.978, respectively, with an image preprocessing time of only 1.21 s and a CPU utilization rate of only 35.5%. This method demonstrates excellent performance across multiple aspects.

Keywords

• Generalized Huber-MRF
• Image modelling
• Super-resolution recovery
• Virtual scene rendering technology