Abstract
In order to improve the spatiotemporal resolution of the video sequences, a novel spatiotemporal super-resolution reconstruction model (STSR) based on robust optical flow and Zernike moment is proposed in this paper, which integrates the spatial resolution reconstruction and temporal resolution reconstruction into a unified framework. The model does not rely on accurate estimation of subpixel motion and is robust to noise and rotation. Moreover, it can effectively overcome the problems of hole and block artifacts. First we propose an efficient robust optical flow motion estimation model based on motion details preserving, then we introduce the biweighted fusion strategy to implement the spatiotemporal motion compensation. Next, combining the self-adaptive region correlation judgment strategy, we construct a fast fuzzy registration scheme based on Zernike moment for better STSR with higher efficiency, and then the final video sequences with high spatiotemporal resolution can be obtained by fusion of the complementary and redundant information with nonlocal self-similarity between the adjacent video frames. Experimental results demonstrate that the proposed method outperforms the existing methods in terms of both subjective visual and objective quantitative evaluations.
Highlights
The resolution quality of the video sequences, which are collected by multisource vision sensors, plays an important role in the accurate moving targets recognition and tracking of the intelligent monitoring and control system
Combining the self-adaptive region correlation judgment strategy, we construct a fast fuzzy registration scheme based on Zernike moment for better super-resolution reconstruction model (STSR) with higher efficiency, and the final video sequences with high spatiotemporal resolution can be obtained by fusion of the complementary and redundant information with nonlocal self-similarity between the adjacent video frames
Considering good properties of rotation, translation, and scale-invariance of Zernike moment (ZM) [17, 18], we propose a fast fuzzy registration scheme based on ZM by using the self-adaptive region correlation judgment strategy, which could make an efficient similarity measure between region features in the spatiotemporal nonlocal domain for weight calculation
Summary
The resolution quality of the video sequences, which are collected by multisource vision sensors, plays an important role in the accurate moving targets recognition and tracking of the intelligent monitoring and control system. Some scholars have proposed a novel fuzzy registration scheme for probabilistic estimation of motion based on similarity match and introduced it into the super-resolution methods [12] for further improving the spatial resolution of image or video, which can effectively avoid the accurate estimation of subpixel motion. We construct a novel spatiotemporal SR reconstruction model based on robust optical flow and ZM, which makes full use of the nonlocal self-similarity and redundant information in the different spatiotemporal scales between the adjacent video frames and produces video sequences with high resolution via fusion of several LR video frames.
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