Earticle

초록

영어

Since cases being analyzed and detected by the police using surveillance videos, insufficient resolution and degradation always lead to the suspect’s face being unrecognized. For such cases, from the perspective of learning-based face super-resolution method, this paper proposed a novel face restored solution based on face super-resolution and image mosaic. Firstly, by applying global similarity selection over the entire database, the scope of the used samples can be narrowed and boost the speed. Then the local representation between neighbors can make the method more robust against noise under severe conditions. Finally the reconstructed face could be inserted into original background by the improved Poisson Editing algorithm. This scheme achieve the effect of face restoring on a single frame in surveillance videos. Experiments under common database and real surveillance video scenarios demonstrate our method has better performance, especially for noise input images.

목차

Abstract
 1. Introduction
 2. Face Restoring Algorithm
 3. Face Hallucination via Similar Selection and Representation
  3.1. Formulation
  3.2. Global Similarity Selection
  3.3. Local Similarity Representation
 4. Face Mosaic Based on Poisson Equation
 5. Experiment and Results
 6. Conclusion
 References

키워드

저자

• He Peng [ College of Telecommunication and Electronic Engineering, Qiqihar University, Qiqihar, China ]
• Dong Wen [ College of Telecommunication and Electronic Engineering, Qiqihar University, Qiqihar, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605