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Abstract
Video surveillance is an important data source of urban computing and intelligence. The low resolution of many existing video surveillance devices affects the efficiency of urban computing and intelligence. Therefore, improving the resolution of video surveillance is one of the important tasks of urban computing and intelligence. In this paper, the resolution of video is improved by superresolution reconstruction based on a learning method. Different from the superresolution reconstruction of static images, the superresolution reconstruction of video is characterized by the application of motion information. However, there are few studies in this area so far. Aimed at fully exploring motion information to improve the superresolution of video, this paper proposes a superresolution reconstruction method based on an efficient subpixel convolutional neural network, where the optical flow is introduced in the deep learning network. Fusing the optical flow features between successive frames can compensate for information in frames and generate high-quality superresolution results. In addition, in order to improve the superresolution, a superpixel convolution layer is added after the deep convolution network. Finally, experimental evaluations demonstrate the satisfying performance of our method compared with previous methods and other deep learning networks; our method is more efficient.
Highlights
Superresolution reconstruction is generating high-resolution results from the low-resolution images using construction models
To improve the superresolution reconstruction method in speed and quality, this paper proposed a new method based on optical flow and efficient subpixel convolutional neural network (ESPCN)
Aimed at improving the performance of the reconstruction of videos, this paper proposes deep convolutional networkbased reconstruction methods where the motion information is extracted and introduced
Summary
Superresolution reconstruction is generating high-resolution results from the low-resolution images using construction models. Algorithm-based image construction can efficiently generate high-resolution images at a low cost. The principle of neighborhood embedding [3] is to make the assumption that the local spatial structure between the low-resolution image block and the high-resolution image block is similar. Yu and Zhang proposed an improved glowworm swarm optimization algorithm for superresolution reconstruction of video images [4]. The above methods can solve some problems of superresolution reconstruction, the disadvantage of the neighborhood embedding method is that the number k of image blocks selected for low-resolution image blocks is artificially specified, which will affect the reconstruction effect by the supervisor and may cause the phenomenon of underfitting and overfitting.
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