Enhancing the Performance of Convolutional Neural Networks on Quality Degraded Datasets

18 Oct 2017 Jonghwa Yim Kyung-Ah Sohn

Despite the appeal of deep neural networks that largely replace the traditional handmade filters, they still suffer from isolated cases that cannot be properly handled only by the training of convolutional filters. Abnormal factors, including real-world noise, blur, or other quality degradations, ruin the output of a neural network... (read more)

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