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구조 인식 심층 합성곱 신경망 기반의 영상 잡음 제거

Image Denoising Via Structure-Aware Deep Convolutional Neural Networks

  • 박기태 (군산대학교 소프트웨어융합공학과) ;
  • 손창환 (군산대학교 소프트웨어융합공학과)
  • 투고 : 2018.08.28
  • 심사 : 2018.10.04
  • 발행 : 2018.11.30

초록

스마트폰의 보급이 확산되고 대중화됨에 따라 대부분의 사람들은 사진을 촬영하기 위해 모바일 카메라를 애용하고 있다. 하지만 저조도 환경에서 사진을 촬영할 때 광량이 부족한 이유로 원치 않는 잡음이 발생할 수 있다. 이런 잡음을 제거하기 위해, 최근 심층 합성곱 신경망에 기반한 잡음 제거 기법이 제안되었다. 이 기법은 성능 측면에서 큰 진전을 보였을지라도 여전히 텍스처 및 에지 표현 능력이 부족하다. 따라서 본 논문에서는 영상의 구조를 향상시키기 위해 에지의 방향 정보를 나타내는 호그 영상을 활용하고자 한다. 그리고 잡음 영상과 호그 영상을 스택으로 쌓은 후, 입력 텐서를 형성하여 심층 합성곱 신경망을 학습시키는 기법을 제안하고자 한다. 실험 결과를 통해, 제안한 기법은 기존의 기법보다 정량적인 화질 평가에서 더 우수한 결과를 얻을 수 있었으며 시각적인 측면에서도 텍스처 및 에지의 향상을 달성할 수 있었다.

With the popularity of smartphones, most peoples have been using mobile cameras to capture photographs. However, due to insufficient amount of lights in a low lighting condition, unwanted noises can be generated during image acquisition. To remove the noise, a method of using deep convolutional neural networks is introduced. However, this method still lacks the ability to describe textures and edges, even though it has made significant progress in terms of visual quality performance. Therefore, in this paper, the HOG (Histogram of Oriented Gradients) images that contain information about edge orientations are used. More specifically, a method of learning deep convolutional neural networks is proposed by stacking noise and HOG images into an input tensor. Experiment results confirm that the proposed method not only can obtain excellent result in visual quality evaluations, compared to conventional methods, but also enable textures and edges to be improved visually.

키워드

과제정보

연구 과제 주관 기관 : 한국연구재단

참고문헌

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피인용 문헌

  1. Multi-Stream Networks with Multi-Loss Layers and Image Structure Map Predictor for Inverse Halftoning vol.17, pp.4, 2018, https://doi.org/10.14801/jkiit.2019.17.4.43