한국인터넷방송통신학회논문지 (The Journal of the Institute of Internet, Broadcasting and Communication)

  • 제18권2호
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  • Pages.117-123
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  • 2018
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  • 2289-0238(pISSN)
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  • 2289-0246(eISSN)
한국인터넷방송통신학회 (The Institute of Internet, Broadcasting and Communication)
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이미지 기울기에서 선별된 포아송 모델을 이용한 이미지 재구성

Image Reconstruction Using Poisson Model Screened from Image Gradient

  • 김용길 (조선이공대학교 컴퓨터보안과)
  • Kim, Yong-Gil (Dept.of computer Security, Chosun college of science & technology)
  • 투고 : 2018.01.15
  • 심사 : 2018.04.06
  • 발행 : 2018.04.30
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초록

본 연구에서는 이미지 기울기 영역에서 포아송 방정식을 이용한 빠른 이미지 재구성 기법을 제안한다. 포아송 방정식을 사용하는 이 접근법에서, 유도된 벡터 필드는 제 1 단계에서 선택된 영역 내에서 원본과 대상 이미지를 사용함으로써 생성된다. 다음으로, 유도된 벡터는 결과 이미지를 생성하는데 사용된다. 우리는 원하는 기울기 집합과 데이터 항을 근사화하는 2차원 함수를 재구성하는 문제를 분석했다. 결합된 데이터와 기울기는 원본 이미지에 가깝게 머무르는 동안 이미지 기울기를 수정하는 것처럼 작동 할 수 있다. 이 공식으로부터 우리는 물리학에서 알려진 포아송 방정식을 찾아냈다. 이 방정식은 FFT 도메인의 문제에 대한 효율적인 해결책을 제시한다. 이것은 2차원으로 알려진 포아송 모델을 해결하고 기울기 비례축소는 라플라스를 확실하게 일반화하는 잘 정의된 선명한 필터임을 공간 필터에 잘 나타냅니다. 포아송 모델을 기반으로 이산 코사인 변환을 사용하여 결과를 확인할 수 있었다.

In this study, we suggest a fast image reconstruction scheme using Poisson equation from image gradient domain. In this approach, using the Poisson equation, a guided vector field is created by employing source and target images within a selected region at the first step. Next, the guided vector is used in generating the result image. We analyze the problem of reconstructing a two-dimensional function that approximates a set of desired gradients and a data term. The joined data and gradients are able to work like modifying the image gradients while staying close to the original image. Starting with this formulation, we have a screened Poisson equation known in physics. This equation leads to an efficient solution to the problem in FFT domain. It represents the spatial filters that solve the two-dimensional screened Poisson model and shows gradient scaling to be a well-defined sharpen filter that generalizes Laplace sharpening. We demonstrate the results using a discrete cosine transformation based this Poisson model.

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