전자공학회논문지 (Journal of the Institute of Electronics and Information Engineers)

  • 제51권8호
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  • Pages.87-96
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  • 2014
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  • 2287-5026(pISSN)
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  • 2288-159X(eISSN)
대한전자공학회 (The Institute of Electronics and Information Engineers)
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사전 학습과 공간-주파수 분석을 사용한 방향 적응적 에일리어싱 및 잡음 제거

Directionally Adaptive Aliasing and Noise Removal Using Dictionary Learning and Space-Frequency Analysis

  • 채은정 (중앙대학교 첨단영상대학원) ;
  • 이은성 (중앙대학교 첨단영상대학원) ;
  • 정혜진 (중앙대학교 첨단영상대학원) ;
  • 백준기 (중앙대학교 첨단영상대학원)
  • Chae, Eunjung (Dept. of Image Engineering, Graduate School of Advanced Image Science, Multimedia, and Film, Chung-Ang University) ;
  • Lee, Eunsung (Dept. of Image Engineering, Graduate School of Advanced Image Science, Multimedia, and Film, Chung-Ang University) ;
  • Cheong, Hejin (Dept. of Image Engineering, Graduate School of Advanced Image Science, Multimedia, and Film, Chung-Ang University) ;
  • Paik, Joonki (Dept. of Image Engineering, Graduate School of Advanced Image Science, Multimedia, and Film, Chung-Ang University)
  • 투고 : 2014.05.19
  • 심사 : 2014.07.30
  • 발행 : 2014.08.25
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초록

본 논문은 다양한 영상 획득 과정에서 발생하는 에일리어싱 성분과 잡음을 동시에 제거하기 위하여 공간-주파수 분석 기반사전 학습(dictionary learning)을 사용한 방향 적응적 영상 개선 알고리듬을 제안한다. 제안된 기술은 i) 학습된 사전과 결합된 웨이블릿-푸리에 변환을 이용하여 에일리어싱 및 잡음 영역을 검출하는 단계와, ii) 검출된 영역에서 방향 적응적 계수 축소기법을 이용하여 에일리어싱을 제거하는 동시에 잡음을 억제하는 단계로 구성된다. 제안한 방법은 공간-주파수 성분을 동시에 분석하여 특정 위치와 특정 주파수 성분을 선택적으로 제거하기 때문에, 검출된 영역에서 에지 성분을 보존하면서 에일리어싱 제거와 잡음 억제를 가능하게 한다. 실험 결과를 근거로 제안된 방법은 기존 알고리듬들과 비교할 때 주요 고주파 성분들의 억제 및 아티펙트 발생을 최소화하며 에일리어싱과 잡음을 제거함으로써 디지털 영상의 리샘플링, 초고해상도 영상 생성, 로봇비전 등과 같은 다양한 영상 획득 장치에 적용될 수 있다.

In this paper, we propose a directionally adaptive aliasing and noise removal using dictionary learning based on space-frequency analysis. The proposed aliasing and noise removal algorithm consists of two modules; i) aliasing and noise detection using dictionary learning and analysis of frequency characteristics from the combined wavelet-Fourier transform and ii) aliasing removal with suppressing noise based on the directional shrinkage in the detected regions. The proposed method can preserve the high-frequency details because aliasing and noise region is detected. Experimental results show that the proposed algorithm can efficiently reduce aliasing and noise while minimizing losses of high-frequency details and generation of artifacts comparing with the conventional methods. The proposed algorithm is suitable for various applications such as image resampling, super-resolution image, and robot vision.

키워드

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