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http://dx.doi.org/10.15207/JKCS.2019.10.5.017

Image Denoising Methods based on DAECNN for Medication Prescriptions  

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)
Publication Information
Journal of the Korea Convergence Society / v.10, no.5, 2019 , pp. 17-26 More about this Journal
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.
Keywords
ROI; DAECNN; SSIM; PSNR; MSE;
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