{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/image-inpainting-with-edge-guided-learnable","title":"Image Inpainting with Edge-guided Learnable Bidirectional Attention Maps","arxiv_id":"2104.12087","date":"2021-04-25","proceeding":null,"authors":["Dongsheng Wang","Chaohao Xie","Shaohui Liu","Zhenxing Niu","WangMeng Zuo"],"abstract":"For image inpainting, the convolutional neural networks (CNN) in previous methods often adopt standard convolutional operator, which treats valid pixels and holes indistinguishably. As a result, they are limited in handling irregular holes and tend to produce color-discrepant and blurry inpainting result. Partial convolution (PConv) copes with this issue by conducting masked convolution and feature re-normalization conditioned only on valid pixels, but the mask-updating is handcrafted and independent with image structural information. In this paper, we present an edge-guided learnable bidirectional attention map (Edge-LBAM) for improving image inpainting of irregular holes with several distinct merits. Instead of using a hard 0-1 mask, a learnable attention map module is introduced for learning feature re-normalization and mask-updating in an end-to-end manner. Learnable reverse attention maps are further proposed in the decoder for emphasizing on filling in unknown pixels instead of reconstructing all pixels. Motivated by that the filling-in order is crucial to inpainting results and largely depends on image structures in exemplar-based methods, we further suggest a multi-scale edge completion network to predict coherent edges. Our Edge-LBAM method contains dual procedures,including structure-aware mask-updating guided by predict edges and attention maps generated by masks for feature re-normalization.Extensive experiments show that our Edge-LBAM is effective in generating coherent image structures and preventing color discrepancy and blurriness, and performs favorably against the state-of-the-art methods in terms of qualitative metrics and visual quality.","url_abs":"https://arxiv.org/abs/2104.12087v1","url_pdf":"https://arxiv.org/pdf/2104.12087v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"image-inpainting-with-edge-guided-learnable","repo_url":"https://github.com/wds1998/Edge-LBAM","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"image-inpainting-with-edge-guided-learnable","repo_url":"https://github.com/MindSpore-scientific/code-12/tree/main/LECF","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"image-inpainting-with-edge-guided-learnable","repo_url":"https://github.com/MindSpore-scientific/code-12/tree/main/LIE-IQA","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"image-inpainting","task_name":"Image Inpainting"},{"task_slug":null,"task_name":"valid"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"pixel-prediction","method_name":"Inpainting"},{"method_slug":"masked-convolution","method_name":"Masked Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}