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This paper proposes a deep neural network (DNN) approach to restore the 17th century painting masterpieces that have been degraded by the over 100 years in an old paper source. Our DNN approach contains three stages: (1) a pretrained VGG-16 [3] model to extract the features of the original image; (2) a trained gradient response map to estimate the quality of the image; (3) three trained DBN architectures with the same structure, encoder (deep-convolutional encoder), and decoder (deep-convolutional decoder), to restore the image. The DBN architectures are trained end-to-end in an alternating fashion, where the encoder and decoder are trained simultaneously, and only the weights of the decoder are updated at the next stage. The pretrained VGG-16 model is trained on the ImageNet dataset and is suitable for object recognition tasks. A gradient response map is generated at the pretrained VGG-16 stage to estimate the quality of the original image, and the gradient response map is used as the loss function of the decoder. Furthermore, we add three perceptual losses to make the restored image better. The experimental results show that our approach can restore the degraded painting with a restoration error less than 2%.

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