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Deep convolution network for surveillance records super-resolution

Article

Abstract

The aim of image super resolution (SR) is to recover low resolution (LR) input image or video to a visually desirable high-resolution (HR) one. The task of identifying an object in surveillance records is interesting, yet challenging due to the low resolution of the video. This paper, proposed a deep learning method for resolution recovery, the low-resolution objects and points in the surveillance records are up-sampled using a deep Convolutional Neural Network (CNN) to avoid problems of image boundary the data padded with zeros. The network is trained and tested on two surveillance datasets. Dissimilar to the outdated methods which operate components individually, our model performs combined optimization for all the layers. The proposed CNN model has a lightweight structure and minimal data pre-processing and computation cost. Testing our model and comparing with advanced techniques, we observed promising results. The code is accessible at https://github.com/Mzareapoor/Super-resolution

Keywords

Super-resolution Convolution neural networks Surveillance records Deep learning 

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute of Image Processing and Pattern RecognitionShanghai Jiao Tong UniversityShanghaiChina
  2. 2.Advanced Scientific Computing DivisionEuro-Mediterranean Centre on Climate ChangeLecceItaly
  3. 3.Institute of AutomationChinese Academy of SciencesBeijingChina
  4. 4.Jaypee University of Engineering and TechnologyGunaIndia

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