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http://hdl.handle.net/10397/95954
Title: | DeepGIN : deep generative inpainting network for extreme image inpainting | Authors: | Li, CT Siu, WC Liu, ZS Wang, LW Lun, DPK |
Issue Date: | 2020 | Source: | Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2020, v. 12538, p. 5-22 | Abstract: | The degree of difficulty in image inpainting depends on the types and sizes of the missing parts. Existing image inpainting approaches usually encounter difficulties in completing the missing parts in the wild with pleasing visual and contextual results as they are trained for either dealing with one specific type of missing patterns (mask) or unilaterally assuming the shapes and/or sizes of the masked areas. We propose a deep generative inpainting network, named DeepGIN, to handle various types of masked images. We design a Spatial Pyramid Dilation (SPD) ResNet block to enable the use of distant features for reconstruction. We also employ Multi-Scale Self-Attention (MSSA) mechanism and Back Projection (BP) technique to enhance our inpainting results. Our DeepGIN outperforms the state-of-the-art approaches generally, including two publicly available datasets (FFHQ and Oxford Buildings), both quantitatively and qualitatively. We also demonstrate that our model is capable of completing masked images in the wild. | Keywords: | Image inpainting Attention Back projection |
Publisher: | Springer | Journal: | Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) | ISSN: | 0302-9743 | EISSN: | 1611-3349 | DOI: | 10.1007/978-3-030-66823-5_1 | Description: | 16th European Conference on Computer Vision 2020, Online, August 23–28, 2020 | Rights: | © Springer Nature Switzerland AG 2020 This version of the contribution has been accepted for publication, after peer review (when applicable) but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/978-3-030-66823-5_1.Use of this Accepted Version is subject to the publisher’s Accepted Manuscript terms of use https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms |
Appears in Collections: | Conference Paper |
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File | Description | Size | Format | |
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EIE-0092_Li_Deepgin_Deep_Generative.pdf | Pre-Published version | 15.4 MB | Adobe PDF | View/Open |
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