Video super-resolution reconstruction based on correlation learning and spatio-temporal nonlocal similarity

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• 作者：Meiyu Liang ; Junping Du ; Linghui Li
• 关键词：Video super ; resolution ; Semi ; coupled dictionary learning ; Spatio ; temporal nonlocal similarity ; Visual saliency ; Pseudo ; Zernike moment
• 刊名：Multimedia Tools and Applications
• 出版年：2016
• 出版时间：September 2016
• 年：2016
• 卷：75
• 期：17
• 页码：10241-10269
• 全文大小：3,460 KB
• 刊物类别：Computer Science
• 刊物主题：Multimedia Information Systems
  Computer Communication Networks
  Data Structures, Cryptology and Information Theory
  Special Purpose and Application-Based Systems
  
• 出版者：Springer Netherlands
• ISSN：1573-7721
• 卷排序：75

文摘

A novel video super-resolution reconstruction algorithm based on correlation learning and spatio-temporal nonlocal similarity is proposed in this paper. Objective high-resolution (HR) estimates of low-resolution (LR) video frames can be obtained by learning LR-HR correlation mapping and fusing the spatio-temporal nonlocal similarity information between video frames. First, the LR-HR correlation mapping between LR and HR patches is established based on semi-coupled dictionary learning. With the aim of improving algorithm efficiency while guaranteeing super-resolution quality, LR-HR correlation mapping is performed only for the salient object region, and then an improved visual saliency-based nonlocal fuzzy registration scheme using the pseudo-Zernike moment feature and structural similarity is proposed for spatio-temporal similarity matching and fusion. Visual saliency and self-adaptive regional correlation evaluation strategies are used in spatio-temporal similarity matching to improve algorithm efficiency further. Experimental results demonstrate that the proposed algorithm achieves competitive super-resolution quality compared to other state-of-the-art algorithms in terms of both subjective and objective evaluations.