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Gradient-Guided DCNN for Inverse Halftoning and Image Expanding

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Computer Vision – ACCV 2018 (ACCV 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11364))

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Abstract

Inverse halftoning and image expanding refer to the ill-posed problems which restore higher-bit images from lower bit ones. Many scholars have studied these problems so far, but the restored images still suffer either quantization artifacts or fine detail losses. Although recent deep convolutional neural network (DCNN) based methods have shown its advantage in these two problems, it is hard to restore high quality images with fine details if no extra information is feeded to the network. To solve this problem, this paper proposes a gradient-guided DCNN model for inverse halftoning and image expanding. The DCNN model consists of two stages. In the first stage, two subnetworks are designed to explicitly predict the gradient maps of the input image, which account for the detail information of image. In the second stage, the gradient maps, concatenated with the input image, are feeded to another subnetwork to guide the reconstruction of the final results. Experimental results show that our method outperforms the state-of-arts in terms of both visual quality and numerical evaluation. In particular, our method better recovers the fine details of the images.

The work is supported by the National Key R&D Program of China (2018YF-B0203904), NSFC from PRC (61872137, 61502158, 61502157, 61472131, 61772191), Hunan NSF (2017JJ3042), and Science and Technology Key Projects of Hunan Province (2015TP1004, 2015SK2087, 2015JC1001, 2016JC2012).

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Authors and Affiliations

  1. College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan Province, China

    Yi Xiao, Chao Pan, Xianyi Zhu, Zheng Qin & Jin Yuan

  2. College of Electrical and Information Engineering, Hunan University, Changsha, Hunan Province, China

    Yan Zheng

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  1. Yi Xiao
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  2. Chao Pan
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  3. Yan Zheng
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  4. Xianyi Zhu
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  5. Zheng Qin
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  6. Jin Yuan
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Corresponding author

Correspondence to Yan Zheng .

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Editors and Affiliations

  1. IIIT Hyderabad, Hyderabad, India

    C.V. Jawahar

  2. ANU, Canberra, ACT, Australia

    Hongdong Li

  3. Simon Fraser University, Burnaby, BC, Canada

    Greg Mori

  4. ETH Zurich, Zurich, Zürich, Switzerland

    Konrad Schindler

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Xiao, Y., Pan, C., Zheng, Y., Zhu, X., Qin, Z., Yuan, J. (2019). Gradient-Guided DCNN for Inverse Halftoning and Image Expanding. In: Jawahar, C., Li, H., Mori, G., Schindler, K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science(), vol 11364. Springer, Cham. https://doi.org/10.1007/978-3-030-20870-7_13

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  • DOI: https://doi.org/10.1007/978-3-030-20870-7_13

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  • Online ISBN: 978-3-030-20870-7

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