이질적 이미지의 딥러닝 분석을 위한적대적 학습기반 이미지 보정 방법론
Adversarial Learning-Based Image Correction Methodology for Deep Learning Analysis of Heterogeneous Images
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초록
빅데이터 시대의 도래는 데이터에서 스스로 규칙을 배우는 딥러닝의 비약적인 발전을 가능하게 하였으며, 특히 CNN 알고리즘이 거둔 성과는 모델의구조를 넘어 소스 데이터 자체를 조정하는 수준에 이르렀다. 하지만 기존의 이미지 처리 방법은 이미지 데이터 자체를 다룰 뿐, 해당 이미지가 생성된이질적 환경을 충분히 고려하지 않았다. 이질적 환경에서 촬영된 이미지는 동일한 정보임에도 촬영 환경에 따라 각 이미지의 특징(Feature)이 상이하게표현될 수 있다. 이는 각 이미지가 갖는 상이한 환경 정보뿐 아니라 이미지 고유의 정보조차 서로 상이한 특징으로 표현되며, 이로 인해 이들 이미지정보는 서로 잡음(Noise)으로 작용해 모델의 분석 성능을 저해할 수 있음을 의미한다. 따라서 본 논문은 이질적 환경에서 생성된 이미지 데이터들을동시에 사용하는 앤드-투-앤드(End-To-End) 구조의 적대적 학습(Adversarial Learning) 기반의 이미지 색 항상성 모델 성능 향상 방안을 제안한다. 구체적으로 제안 방법론은 이미지가 촬영된 환경인 도메인을 예측하는 ‘도메인 분류기’와 조명 값을 예측하는 ‘조명 예측기’의 상호 작용으로 동작하며,도메인 분류의 성능을 떨어뜨리는 방향의 학습을 통해 도메인 특성을 제거한다. 제안 방법론의 성능을 평가하기 위해 이질적 환경에서 촬영된 이미지데이터 셋 7,022장에 대한 색 항상성 실험을 수행한 결과, 제안 방법론이 기존 방법론에 비해 Angular Error 측면에서 우수한 성능을 나타냄을 확인하였다.
Abstract
The advent of the big data era has enabled the rapid development of deep learning that learns rules by itself from data. In particular, theperformance of CNN algorithms has reached the level of self-adjusting the source data itself. However, the existing image processing methodonly deals with the image data itself, and does not sufficiently consider the heterogeneous environment in which the image is generated. Imagesgenerated in a heterogeneous environment may have the same information, but their features may be expressed differently depending on thephotographing environment. This means that not only the different environmental information of each image but also the same informationare represented by different features, which may degrade the performance of the image analysis model. Therefore, in this paper, we proposea method to improve the performance of the image color constancy model based on Adversarial Learning that uses image data generated ina heterogeneous environment simultaneously. Specifically, the proposed methodology operates with the interaction of the ‘Domain Discriminator’that predicts the environment in which the image was taken and the ‘Illumination Estimator’ that predicts the lighting value. As a result ofconducting an experiment on 7,022 images taken in heterogeneous environments to evaluate the performance of the proposed methodology,the proposed methodology showed superior performance in terms of Angular Error compared to the existing methods.
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