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불규칙 매체를 통한 컴퓨테이셔널 이미징의 최근 연구 동향

Recent Progress in Computational Imaging Through Turbid Media

  • Jang, Hwanchol (School of Information and Communications, Gwangju Institute of Science and Technology) ;
  • Yoon, Changhyeong (Department of Physics, Korea University) ;
  • Chung, Euiheon (Department of Medical System Engineering and School of Mechatronics, Gwangju Institute of Science and Technology) ;
  • Choi, Wonshik (Department of Physics, Korea University) ;
  • Lee, Heung-No (School of Information and Communications, Gwangju Institute of Science and Technology)
  • 투고 : 2014.10.22
  • 심사 : 2014.12.11
  • 발행 : 2014.12.31

초록

불규칙 매체를 투과하는 광학적 이미징 시스템은 피부나 생물학적 조직등의 내부를 비침습적 이미징 기법을 사용해 관찰할 수 있게 해줄 것으로 큰 기대를 받고 있다. 불규칙 매체를 통한 이미징은 대개 불규칙 매체의 투과 특성을 전달 행렬로 모델링 및 측정하고, 측정된 전달 행렬을 사용하여 이미지를 복구하는 방식을 사용한다. 이러한 전달 행렬 기반의 이미징 방법은 많은 양의 데이터를 측정 하고 후 신호 처리를 해야 한다는 어려움을 가지고 있다. 최근에는, 이 데이터 획득 문제를 압축센싱이라는 방법을 사용해 해결할 수 있다는 결과들이 있었다. 압축센싱은 상대적으로 새로운 신호 획득 및 복구 체계로써 아주 적은 양의 신호 측정만으로도 신호를 정확하게 복구해 낼 수 있다. 본 논문에서는 불규칙 매체를 통과하는 이미징에서의 전달 행렬 기반의 이미지 복구 방법이 검토되며, 또한 압축센싱을 사용한 최신 연구 동향을 소개하고자 한다.

It is expected that the techniques of optical imaging through turbid media enables non-invasive imaging through human skin and biological tissues. In recent years, many researches have shown that imaging through turbid media can be made possible by measuring the transmission matrix (TM) of the turbid medium and utilizing it for image recovery. However, this TM based image recovery requires a huge amount of data acquisition and post signal processing of them. Very recently, there were new results that this problem of huge data acquisition and processing can be resolved by using the compressed sensing (CS) framework. CS is a relatively new signal acquisition and reconstruction framework which makes possible to recover the signal of interest correctly with significantly smaller number of signal measurements. In this paper, the TM-based image recovery in imaging through turbid media is reviewed and the recent progress made by using CS is introduced.

키워드

참고문헌

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