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

  • 제53권6호
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  • Pages.110-121
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  • 2016
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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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RGB 채널 표준 편차의 최적화를 통한 광원 색도 추정

Illuminant Chromaticity Estimation via Optimization of RGB Channel Standard Deviation

  • 투고 : 2016.02.23
  • 심사 : 2016.06.02
  • 발행 : 2016.06.25
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초록

색 항상성 알고리즘의 주된 목적은 광원의 색도를 추정하는 것으로, 최근 통계 기반과 학습 기반 및 통계와 학습의 조합 기반의 색 항상성 알고리즘들이 다양하게 연구되고 있다. 통계 기반 알고리즘은 특정 가정을 만족하는 영상들에 대해서만 수행이 가능하고, 학습 기반 알고리즘은 정확한 전처리와 학습 데이터가 요구되는 복잡한 방법이다. 그리고 통계와 학습의 조합 기반 알고리즘은 사전에 결정되거나 동적으로 변하는 가중치에 따라 결과가 의존적이기 때문에, 이를 정의하기 어려울 뿐만 아니라 에러에도 민감하다. 따라서 본 논문은 복잡한 전처리를 요구하지 않으며, 다양한 환경 조건 하에서 광원 추정이 가능한 새로운 최적화 방법을 제안한다. 영상 내에서 광원의 영향이 강하게 미치는 부분은 한 채널의 표준 편차가 나머지 두 채널에 비해 큰 차이를 가진다. 이 가정을 기반으로, 광원 정도(DIT, degree of illumiinant tinge)라고 불리는 비용 함수는 광원이 보정된 영상의 질을 결정하기 위해 제안된다. 표준 광원(d65) 하의 영상이 다른 광원 하의 영상에 비해 더 작은 DIT 값을 가진다. 본 논문에서 군집단 최적화(PSO, particle swarm optimization) 기반의 집단지성(swarm intelligence)은 DIT를 최소화하기 위해, 주어진 영상의 최적 광원을 찾는데 사용된다. 제안한 방법은 실세계 데이터셋을 통해 평가하였고, 실험 결과는 제안된 방법의 효율성을 입증하였다.

The primary aim of the color constancy algorithm is to estimate illuminant chromaticity. There are various statistical-based, learning-based and combinational-based color constancy algorithms already exist. However, the statistical-based algorithms can only perform well on images that satisfy certain assumptions, learning-based methods are complex methods that require proper preprocessing and training data, and combinational-based methods depend on either pre-determined or dynamically varying weights, which are difficult to determine and prone to error. Therefore, this paper presents a new optimization based illuminant estimation method which is free from complex preprocessing and can estimate the illuminant under different environmental conditions. A strong color cast always has an odd standard deviation value in one of the RGB channels. Based on this observation, a cost function called the degree of illuminant tinge(DIT) is proposed to determine the quality of illuminant color-calibrated images. This DIT is formulated in such a way that the image scene under standard illuminant (d65) has lower DIT value compared to the same scene under different illuminant. Here, a swarm intelligence based particle swarm optimizer(PSO) is used to find the optimum illuminant of the given image that minimizes the degree of illuminant tinge. The proposed method is evaluated using real-world datasets and the experimental results validate the effectiveness of the proposed method.

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