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Link to original content: https://unpaywall.org/10.1007/S00371-022-02468-4
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MS-GAN: multi-scale GAN with parallel class activation maps for image reconstruction

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Abstract

Recently, image reconstruction has been a research hotspot in the field of deep learning. For image reconstruction, generative adversarial networks (GANs) have obtained some remarkable results, but the existing methods based on GANs have not achieved satisfactory reconstructed results in quality. In order to improve the quality, we propose a more effective multi-scale GAN for image reconstruction and the proposed method is called MS-GAN. The generator of MS-GAN uses an improved U-net to capture the important details from the sparse inputs. In MS-GAN, the parallel class activation maps (P-CAMs) and spectral normalization (SN) are added to U-net. P-CAMs are composed of two parallel class activation maps (CAMs) and can specifically guide the generator to focus on the important details of the images for a more realistic visual effect. For the training process, MS-GAN consists of two phases: the generating phase and the refinement phase. The generating phase is to use binary sparse edges and color domains to generate the preliminary images. The refinement phase is to further improve the quality of the preliminary images. Experimental verifications are conducted on some datasets, which include edges2shoes, edges2handbags and Getchu. Experimental results show that our approach outperforms the existing state-of-the-art methods. The images reconstructed by MS-GAN is more photo-realistic in terms of visual effects.

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Funding

The work described in this paper was funded by 2021 Ministry of Education New Liberal Arts Research and Reform Practice Project "The Construction of Interdisciplinary Innovation and Entrepreneurship Practice Teaching System Based on Design Empowerment" (2021160043) and 2021 Hubei Provincial Department of Education Philosophy and Social Science Youth Project: Research on Chinese Ancient Porcelain Restoration Based on Virtual Simulation Technology (21Q072). Any conclusions or recommendations stated here are those of the authors and do not necessarily reflect the official position of the sponsors (Grant Nos. HBCIR2020Z005 and HBCY1914).

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Authors and Affiliations

  1. School of Arts and Design, Hubei University of Technology, Hubei, 430068, China

    Jian Rao & Yue Ming

  2. School of Computer Science, Hubei University of Technology, Hubei, 430068, China

    Aihua Ke & Gang Liu

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  1. Jian Rao
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  2. Aihua Ke
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Correspondence to Aihua Ke.

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Jian Rao declares that he has no conflict of interest. Aihua Ke declares that she has no conflict of interest. Gang Liu declares that he has no conflict of interest. Yue Ming declares that she has no conflict of interest.

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Rao, J., Ke, A., Liu, G. et al. MS-GAN: multi-scale GAN with parallel class activation maps for image reconstruction. Vis Comput 39, 2111–2126 (2023). https://doi.org/10.1007/s00371-022-02468-4

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