关键词: 全景视频, 超分辨率, 几何校正, 球面对齐, 深度学习

Abstract: Traditional video super-resolution methods were found to be ineffective in addressing the geometric distortion problems caused by equirectangular projection when processing panoramic videos. Deficiencies were also observed in inter-frame alignment and feature fusion, which resulted in poor reconstruction quality. To further improve the super-resolution reconstruction quality of panoramic videos, a panoramic video super-resolution network combining spherical alignment and adaptive geometric correction, named 360GeoVSR, was proposed. Precise alignment and efficient fusion of inter-frame features were achieved through a Spherical Alignment Module (SAM) and a Geometric Fusion Block (GFB). In SAM, spatial transformation and deformable convolution were combined to address global and local geometric distortions. In GFB, feature alignment was dynamically corrected using an embedded Adaptive Geometric Correction submodule (AGC), and multi-frame information was fused to capture complex inter-frame relationships. The results of subjective and objective comparison experiments on the extended ODV360Extended panoramic video dataset showed that 360GeoVSR outperformed five representative super-resolution methods, including BasicVSR++ and VRT (Video Restoration Transformer), in both objective metrics and subjective visual effects, validating its effectiveness. 

Key words: panoramic video, super-resolution, geometric correction, spherical alignment, deep learning