A super-resolution reconstruction algorithm for surveillance images

  • Authors:

  • Liangpei Zhang;Hongyan Zhang;Huanfeng Shen;Pingxiang Li

  • Affiliations:

  • The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei, China;The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei, China;School of Resource and Environmental Science, Wuhan University, Wuhan, Hubei, China;The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei, China

  • Venue:
  • Signal Processing
  • Year:
  • 2010

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Abstract

In many surveillance video applications, it is of interest to recognize a region of interest (ROI), which often occupies a small portion of a low-resolution, noisy video. This paper proposes an edge-preserving maximum a posteriori (MAP) estimation based super-resolution algorithm using a weighted directional Markov image prior model for a ROI from more than one low-resolution surveillance image. Conjugate gradient (CG) optimization based on standard operations on images is then developed to improve the computational efficiency of the algorithm. The proposed algorithm is tested on different series of surveillance images. The experimental results indicate that the proposed algorithm has considerable effectiveness in terms of both objective measurements and visual evaluation.