基于Zernike矩的空间视频序列时空自适应性超分辨率重建模型

Spatio-Temporal Adaptive Super-Resolution Reconstruction Model Based on Zernike Moment for Spatial Video Sequences

Video Super-Resolution (SR) reconstruc-tion produces video sequences with High Resolu-tion (HR) via the fusion of several Low-Resolution (LR) video frames. Traditional methods rely on the accurate estimation of subpixel motion, which con-strains their applicability to video sequences with relatively simple motions such as global translation. We propose an efficient iterative spatio-temporal a-daptive SR reconstruction model based on Zernike Moment (ZM), which is effective for spatial video sequences with arbitrary motion. The model uses re-gion correlation judgment and self-adaptive thresh-old strategies to improve the effect and time effi-ciency of the ZM-based SR method. This leads to better mining of non-local self-similarity and local structural regularity, and is robust to noise and rota-tion. An efficient iterative curvature-based interpo-lation scheme is introduced to obtain the initial HR estimation of each LR video frame. Experimental results both on spatial and standard video sequences demonstrate that the proposed method outperforms existing methods in terms of both subjective visual and objective quantitative evaluations, and greatly improves the time efficiency.