Journal of the Institute of Electronics Engineers of Korea SC (전자공학회논문지SC)

The Institute of Electronics and Information Engineers (대한전자공학회)
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Depth From Defocus using Wavelet Transform

웨이블릿 변환을 이용한 Depth From Defocus

  • Choi, Chang-Min (Department of Mechatronics, Gwangju Institute of Science and Technology) ;
  • Choi, Tae-Sun (Department of Mechatronics, Gwangju Institute of Science and Technology)
  • 최창민 (광주과학기술원 기전공학과) ;
  • 최태선 (광주과학기술원 기전공학과)
  • Published : 2005.09.25
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Abstract

In this paper, a new method for obtaining three-dimensional shape of an object by measuring relative blur between images using wavelet analysis has been described. Most of the previous methods use inverse filtering to determine the measure of defocus. These methods suffer from some fundamental problems like inaccuracies in finding the frequency domain representation, windowing effects, and border effects. Besides these deficiencies, a filter, such as Laplacian of Gaussian, that produces an aggregate estimate of defocus for an unknown texture, can not lead to accurate depth estimates because of the non-stationary nature of images. We propose a new depth from defocus (DFD) method using wavelet analysis that is capable of performing both the local analysis and the windowing technique with variable-sized regions for non-stationary images with complex textural properties. We show that normalized image ratio of wavelet power by Parseval's theorem is closely related to blur parameter and depth. Experimental results have been presented demonstrating that our DFD method is faster in speed and gives more precise shape estimates than previous DFD techniques for both synthetic and real scenes.

본 논문에서는 CCD 카메라를 이용하여 획득된 영상들 간의 상대적인 열화(Blur)를 이용하여 물체의 3차원 형상 및 거리 정보를 얻을 수 있는 Depth From Defocus(DFD) 방법을 제안한다. 기존 논문의 주파수 영역에서 디포커스(Defocus) 연산자를 구하는 역필터링(Inverse filtering) 방법은 정확도가 떨어지고, 윈도우 효과(Windowing effects) 및 영상의 경계 효과(Border effect)와 같은 단점이 있었다. 또한 일반적인 영상은 비정체성 (Nonstationary)이기 때문에, 임의의 텍스처에 대한 가우시안(Gaussian) 및 라플라시안(Laplacian) 연산자 등의 필터를 이용하는 디포커스 방법의 추정값은 결과가 좋지 않다. 이러한 문제점들을 해결하기 위해 지역적 분석과 함께 다양한 크기의 윈도우를 제공하는 웨이블릿 변환을 이용한 DFD 방법을 제안한다. 복잡한 텍스처 특성을 갖는 영상의 깊이 추정을 위해서는 웨이블릿 분석을 사용하는 것이 효과적이다. Parseval의 정리에 의해 영상 간의 웨이블릿 에너지의 비율이 열화 계수(Blur parameter) 및 거리와 관련 있음을 증명하였다. 제안된 DFD 알고리즘의 성능을 계산하기 위해 실험은 종합적이며 실제적인 영상을 이용하여 행하였다. 본 논문의 DFD 방식은 기존의 DFD 방법보다 RMS 에러 측면에서 정확한 결과를 보였다.

Keywords

References

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