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http://dx.doi.org/10.5909/JBE.2019.24.3.430

Classification and Restoration of Compositely Degraded Images using Deep Learning  

Yun, Jung Un (Department of Information and Communication Engineering, Inha University)
Nagahara, Hajime (Institute for Datability Science, Osaka University)
Park, In Kyu (Department of Information and Communication Engineering, Inha University)
Publication Information
Journal of Broadcast Engineering / v.24, no.3, 2019 , pp. 430-439 More about this Journal
Abstract
The CNN (convolutional neural network) based single degradation restoration method shows outstanding performance yet is tailored on solving a specific degradation type. In this paper, we present an algorithm of multi-degradation classification and restoration. We utilize the CNN based algorithm for solving image degradation classification problem using pre-trained Inception-v3 network. In addition, we use the existing CNN based algorithms for solving particular image degradation problems. We identity the restoration order of multi-degraded images empirically and compare with the non-reference image quality assessment score based on CNN. We use the restoration order to implement the algorithm. The experimental results show that the proposed algorithm can solve multi-degradation problem.
Keywords
Deep learning; Multi-Degradation; Degradation Classification; Restoration order; Restoration;
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