Journal of the Korea Convergence Society (한국융합학회논문지)

Korea Convergence Society (한국융합학회)
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Image Denoising Methods based on DAECNN for Medication Prescriptions

DAECNN 기반의 병원처방전 이미지잡음제거

  • Khongorzul, Dashdondov (Dept. Computer Engineering, Chungbuk National University) ;
  • Lee, Sang-Mu (Dept. Computer Engineering, Chungbuk National University) ;
  • Kim, Yong-Ki (Dept. Computer Engineering, Chungbuk National University) ;
  • Kim, Mi-Hye (Dept. Computer Engineering, Chungbuk National University)
  • Received : 2019.03.11
  • Accepted : 2019.05.20
  • Published : 2019.05.28
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Abstract

We aimed to build a patient-based allergy prevention system using the smartphone and focused on the region of interest (ROI) extraction method for Optical Character Recognition (OCR) in the general environment. However, the current ROI extraction method has shown good performance in the experimental environment, but the performance in the real environment was not good due to the noisy background. Therefore, in this paper, we propose the compared methods of reducing noisy background to solve the ROI extraction problem. There five methods used as a SMF, DIN, Denoising Autoencoder(DAE), DAE with Convolution Neural Network(DAECNN) and median filter(MF) with DAECNN (MF+DAECNN). We have shown that our proposed DAECNN and MF+DAECNN methods are 69%, respectively, which is relatively higher than the conventional DAE method 55%. The verification of performance improvement uses MSE, PSNR and SSIM. The system has implemented OpenCV, C++ and Python, including its performance, is tested on real images.

본 연구는 환자의 알레르기 예방시스템을 구축하기 위해 스마트폰을 이용하여 저장된 처방전의 이미지잡음제거를 위한 ROI 추출 방법에 중점을 두었다. 현재 ROI 추출은 제한된 실험 환경에서 좋은 성능을 보여 주었지만 실제 환경에서의 성능은 잡음으로 인해 좋지 않았다. 따라서 본 연구에서는 정확도 높은 ROI 추출을 위해 스마트폰 영상에서 발생하는 잡음제거 방법을 제안한다. SMF, DIN, DAE, DAECNN(Denoising Autoencoder with Convolution Neural Network) and median filter with DAECNN(MF+DAECNN) 방법을 실험하였고 그 결과 DAECNN 및 MF + DAECNN 방법이 스마트폰에서 이미지의 잡음제거가 효과적임을 보여주었다. 성능 향상을 검증하기 위해 SSIM, PSNR 및 MSE 방법을 사용하였고 이 시스템은 OpenCV, C ++ 및 Python로 구현 및 실험되었고 실제 이미지에서 성능 테스트를 거쳐 자연잡음(natural noise)을 제거하는데 본 논문에서 제안한 DAECNN과 MF+DAECNN이 각 69%로 기존의 DAE 방법 55% 보다 상대적으로 높은 결과를 도출하였다.

Keywords

OHHGBW_2019_v10n5_17_f0001.png 이미지

Fig. 1. Dataset structure

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Fig. 2. Sample Images of Real Dataset

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Fig. 3. System architecture

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Fig. 4. Standard Median Filter

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Fig. 5. Structure of light illumination normalization

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Fig. 6. A Denoising Autoencoder

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Fig. 7. Testing loss comparison of DAE, DAECNN and MF+DAECNN methods

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Fig. 8. Sample of Noisy and Denoised images fordifferent denoising methods.

Table 1. MSE comparison results of different denoising methods with the original image on Eq. 2.

OHHGBW_2019_v10n5_17_t0001.png 이미지

Table 2. PSNR comparison results of different denoising methods with the original image on Eq. 3.

OHHGBW_2019_v10n5_17_t0002.png 이미지

Table 3. SSIM comparison results of different denoising methods with the original image on Eq. 4.

OHHGBW_2019_v10n5_17_t0003.png 이미지

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