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Long-Distance Object Recognition With Image Super Resolution: A Comparative Study
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DOI：10.1109/ACCESS.2018.2799861.png)
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摘要
En 中文
Monitor systems are ubiquitously deployed in public areas. However, monitor systems face a major challenge regarding long-distance object recognition. Super-resolution constitutes a popular choice to address this challenge. Since super-resolution methods are used in many applications, it is necessary to understand these methods and make a comparative study of them. In this paper, we perform a comparative study on six super-resolution methods over two recognition algorithms. The paper evaluates super-resolution performance based on recognition accuracy, and serves as a summary assessment of image super-resolution algorithms.
Keyword:
Super-resolution
sparse representation
deep learning
convolutional neural networks
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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