Learning-Based Video Superresolution Reconstruction Using Spatiotemporal Nonlocal Similarity

Aiming at improving the video visual resolution quality and details clarity, a novel learning-based video superresolution reconstruction algorithm using spatiotemporal nonlocal similarity is proposed in this paper. Objective high-resolution (HR) estimations of low-resolution (LR) video frames can be obtained by learning LR-HR correlation mapping and fusing spatiotemporal nonlocal similarities between video frames. With the objective of improving algorithm efficiency while guaranteeing superresolution quality, a novel visual saliency-based LR-HR correlation mapping strategy between LR and HR patches is proposed based on semicoupled dictionary learning. Moreover, aiming at improving performance and efficiency of spatiotemporal similarity matching and fusion, an improved spatiotemporal nonlocal fuzzy registration scheme is established using the similarity weighting strategy based on pseudo-Zernike moment feature similarity and structural similarity, and the self-adaptive regional correlation evaluation strategy. The proposed spatiotemporal fuzzy registration scheme does not rely on accurate estimation of subpixel motion, and therefore it can be adapted to complex motion patterns and is robust to noise and rotation. Experimental results demonstrate that the proposed algorithm achieves competitive superresolution quality compared to other state-of-the-art algorithms in terms of both subjective and objective evaluations.