Journal of Broadcast Engineering (방송공학회논문지)

The Korean Institute of Broadcast and Media Engineers (한국방송∙미디어공학회)
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Uniform Motion Deblurring using Shock Filter and Convolutional Neural Network

쇼크 필터와 합성곱 신경망 기반의 균일 모션 디블러링 기법

  • Jeong, Minso (Department of Electronics and Computer Engineering, Hanyang University) ;
  • Jeong, Jechang (Department of Electronics and Computer Engineering, Hanyang University)
  • 정민소 (한양대학교 전자컴퓨터통신공학과) ;
  • 정제창 (한양대학교 전자컴퓨터통신공학과)
  • Received : 2018.05.04
  • Accepted : 2018.07.09
  • Published : 2018.07.30
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Abstract

The uniform motion blur removing algorithm of Cho et al. has the problem that the edge region of the image cannot be restored clearly. We propose the effective algorithm to overcome this problem by using shock filter that reconstructs a blurred step signal into a sharp edge, and convolutional neural network (CNN) that learns by extracting features from the image. Then uniform motion blur kernel is estimated from the latent sharp image to remove blur in the image. The proposed algorithm improved the disadvantages of the conventional algorithm by reconstructing the latent sharp image using shock filter and CNN. Through the experimental results, it was confirmed that the proposed algorithm shows excellent reconstruction performance in objective and subjective image quality than the conventional algorithm.

Cho 등의 균일 모션 블러 제거 알고리듬은 영상 내 외곽선 영역을 선명하게 복원하지 못한다는 문제점이 있다. 이러한 문제점을 극복하기 위해 본 논문에서는 한 장의 정지 영상에서 발생하는 블러 (Blur)현상을 블러된 계단형 신호를 뚜렷한 외곽선으로 복원해주는 쇼크 필터 (Shock filter)와 영상에서 특징을 추출하여 학습하는 합성곱 신경망 (Convolutional Neural Network: CNN)을 이용하여 선명한 영상을 복원하고 이 영상으로부터 균일 모션 (Uniform motion) 블러를 측정하여 영상 내 블러 현상을 제거하는 효과적인 알고리듬을 제안하고자 한다. 제안된 알고리듬은 쇼크 필터와 합성곱 신경망을 이용하여 선명한 영상을 복원함으로써 기존 알고리듬의 단점을 개선하였다. 실험 결과를 통해 제안하는 알고리듬이 기존 알고리듬에 비해 객관적 및 주관적인 평가에서 우수한 복원 성능을 나타냄을 확인하였다.

Keywords

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