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Regularization-based Superresolution Demosaicing using Aperture Mask Wheels

조리개 마스크 휠을 이용한 정칙화 기반 초해상도 디모자이킹

  • Shin, Jeongho (Dept. of Computer Engineering, Hankyong National University)
  • 신정호 (한경대학교 컴퓨터공학과)
  • Received : 2017.12.11
  • Accepted : 2018.01.08
  • Published : 2018.01.30

Abstract

This paper presents a superresolution demosaicing technique that can restore high-resolution color image from differently blurred low resolution images in Bayer domain. The proposed superresolution demosaicing algorithm uses an aperture mask wheel to get differently blurred low resolution images, so we just need to estimate point spread function at each frame. In addition, it does not require image registration because there is no translational motion between low resolution images. By using a rotatable aperture mask wheel, consecutive captured images provide sufficiently exclusive information for superresolution. Therefore, the proposed method can reduce the registration error between the low-resolution image as well as the calculation amount for superresolution restoration. The existing lens system of the camera can be extended to obtain a superresolution image by only adding an rotatable aperture mask wheels. Finally, in order to verify the performance of the proposed system, experimental results are performed. The proposed method showed the significant improvements in the sense of spatial and color resolution.

본 논문에서는 베이어 영역에서의 저해상도 영상을 고해상도 컬러 영상으로 복원하기 위한 초해상도 디모자이킹 기술을 제안한다. 제안한 방법은 조리개 마스크 휠의 다양한 형태의 조리개 마스크로 초점 열화가 발생되기 때문에 취득한 저해상도 영상들은 서로 다른 방향의 초점열화를 가지며 부화소 단위의 영상 정합이 필요하지 않고 단지 조리개 마스크 형태에 해당하는 초점열화만 추정하면 초해상도 영상복원 방법을 적용할 수 있다. 제안한 시스템은 기존의 카메라 렌즈 시스템에 조리개 마스크 휠을 추가함으로써 새로운 형태의 렌즈 시스템을 제작할 필요 없이 초해상도 영상을 복원할 수 있는 카메라 시스템으로 확장 가능하다. 마지막으로 본 논문에서 제안한 조리개 마스크를 이용한 사용한 초해상도 디모자이킹 기술의 성능을 검증하기 위해서 기존의 초해상도 영상복원과 디모자이킹 기술과 비교하였으며, 그 결과 공간 및 컬러 해상도가 상당히 개선되었음을 보였다.

Keywords

References

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