Abstract
Inverse halftoning as a classic problem has been investigated in the last two decades, however, it is still a challenge to recover the continuous version with accurate details from halftone images. In this paper, we present a statistic learning based method to address it, leveraging Convolutional Neural Network (CNN) as a nonlinear mapping function. To exploit features as completely as possible, we propose a Progressively Residual Learning (PRL) network that synthesizes the global tone and subtle details from the halftone images in a progressive manner. Particularly, it contains two modules: Content Aggregation that removes the halftone patterns and reconstructs the continuous tone firstly, and Detail Enhancement that boosts the subtle structures incrementally via learning a residual image. Benefiting from this efficient architecture, the proposed network is superior to all the candidate networks employed in our experiments for inverse halftoning. Also, our approach outperforms the state of the art with a large margin.
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Acknowledgement
This project is supported by Shenzhen Science and Technology Program (No. JCYJ20160429190300857) and Shenzhen Key Laboratory (No. ZDSYS201605101739178), and the Research Grants Council of the Hong Kong Special Administrative Region, under RGC General Research Fund (Project No. CUHK14201017).
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Xia, M., Wong, TT. (2019). Deep Inverse Halftoning via Progressively Residual Learning. In: Jawahar, C., Li, H., Mori, G., Schindler, K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science(), vol 11366. Springer, Cham. https://doi.org/10.1007/978-3-030-20876-9_33
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