Journal of Digital Contents Society (디지털콘텐츠학회 논문지)

Digital Contents Society (한국디지털콘텐츠학회)
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Super Resolution Algorithm using TV-G Decomposition

TV-G 분해를 이용한 초해상도 알고리즘

  • Eum, Kyoung-Bae (Department of Computer and Information Eng., Kunsan National University,) ;
  • Beom, Dong-Kyu (Department of Computer and Information Eng., Kunsan National University,)
  • 엄경배 (군산대학교 컴퓨터정보공학과) ;
  • 범동규 (군산대학교 컴퓨터정보공학과)
  • Received : 2017.11.17
  • Accepted : 2017.12.25
  • Published : 2017.12.31
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Abstract

Among single image SR techniques, the TV based SR approach seems most successful in terms of edge preservation and no artifacts. But, this approach achieves insufficient SR for texture component. In this paper, we proposed a new TV-G decomposition based SR method to solve this problem. We proposed the SVR based up-sampling to get better edge preservation in the structure component. The NNE used the relaxed constraint to improve the NE. We used the NNE based learning method to improve the resolution of the texture component. Through experimental results, we quantitatively and qualitatively confirm the improved results of the proposed SR method when comparing with conventional interpolation method, ScSR, TV and NNE.

단일 영상 기반 초해상도(SR) 기법 중 TV 기반 초해상도 기법은 에지 보존과 artifact가 없다는 점에서 성공적인 방법으로 평가되어 왔으나, 텍스쳐 성분에서는 개선을 보이지 못했다. 본 논문에서는 이와 같은 문제점을 개선하기 위해서 새로운 TV-G 분해 기반 초해상도 기법을 제안하였다. 제안된 초해상도 방법에서는 에지와 같은 구조적 성분의 해상도를 보다 더 개선하기 위해 SVR 기반 up-sampling 방법을 제안하였다. 또한, Neighbor Embedding(NE)을 개선하기 위해 완화된 제약조건을 이용한 Non-negative Embedding(NNE) 방법에 기반한 학습 방법을 이용하여 텍스쳐 성분의 해상도를 개선하였다. 실험을 통하여 본 논문에서 제안된 방법이 기존의 보간법, ScSR, 기존의 TV 및 NNE 기법들에 비해 정량적인 척도 및 시각적으로도 향상된 좋은 결과들을 보였다.

Keywords

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