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http://dx.doi.org/10.9717/kmms.2022.25.1.029

Image Restoration Method using Denoising CNN  

Kim, Seonjae (Department of Computer Engineering, Dong-A University)
Lee, Jeongho (Department of Convergence IT Engineering, Kyungnam University)
Lee, Suk-Hwan (Department of Computer Engineering, Dong-A University)
Jun, Dongsan (Department of Computer Engineering, Dong-A University)
Publication Information
Journal of Korea Multimedia Society / v.25, no.1, 2022 , pp. 29-38 More about this Journal
Abstract
Although image compression is one of the essential technologies to transmit image data on a variety of surveillance and mobile healthcare applications, it causes unnecessary compression artifacts such as blocking and ringing artifacts by the lossy compression in the limited network bandwidth. Recently, image restoration methods using convolutional neural network (CNN) show the significant improvement of image quality from the compressed images. In this paper, we propose Image Denoising Convolutional Neural Networks (IDCNN) to reduce the compression artifacts for the purpose of improving the performance of object classification. In order to evaluate the classification accuracy, we used the ImageNet test dataset consisting of 50,000 natural images and measured the classification performance in terms of Top-1 and Top-5 accuracy. Experimental results show that the proposed IDCNN can improve Top-1 and Top-5 accuracy as high as 2.46% and 2.42%, respectively.
Keywords
Convolutional Neural Networks; Image Classification; Artifact Reduction; Image Denoising; Image Restoration;
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