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Classification-based video super-resolution using artificial neural networks
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DOI：10.1016/j.sigpro.2013.02.013.png)
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In this study, a classification-based video super-resolution method using artificial neural network (ANN) is proposed to enhance low-resolution (LR) to high-resolution (HR) frames. The proposed method consists of four main steps: classification, motion-trace volume collection, temporal adjustment, and ANN prediction. A classifier is designed based on the edge properties of a pixel in the LR frame to identify the spatial information. To exploit the spatio-temporal information, a motion-trace volume is collected using motion estimation, which can eliminate unfathomable object motion in the LR frames. In addition, temporal lateral process is employed for volume adjustment to reduce unnecessary temporal features. Finally, ANN is applied to each class to learn the complicated spatio-temporal relationship between LR and HR frames. Simulation results show that the proposed method successfully improves both peak signal-to-noise ratio and perceptual quality. (c) 2013 Elsevier B.V. All rights reserved.
Keyword:
Artificial neural network (ANN)
Bilateral filter
Classification
Motion estimation
Super-resolution
期刊
IF:
3.6
论文数:
10.0K
被引数:
1.7W
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Video super-resolution reconstruction using a mobile search strategy and adaptive patch size.png)
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