Bridging the Visual Gap: Wide-Range Image Blending

Chia Ni Lu, Ya Chu Chang, Wei Chen Chiu

研究成果: Conference contribution同行評審

17 引文 斯高帕斯(Scopus)

摘要

In this paper we propose a new problem scenario in image processing, wide-range image blending, which aims to smoothly merge two different input photos into a panorama by generating novel image content for the intermediate region between them. Although such problem is closely related to the topics of image inpainting, image outpainting, and image blending, none of the approaches from these topics is able to easily address it. We introduce an effective deep-learning model to realize wide-range image blending, where a novel Bidirectional Content Transfer module is proposed to perform the conditional prediction for the feature representation of the intermediate region via recurrent neural networks. In addition to ensuring the spatial and semantic consistency during the blending, we also adopt the contextual attention mechanism as well as the adversarial learning scheme in our proposed method for improving the visual quality of the resultant panorama. We experimentally demonstrate that our proposed method is not only able to produce visually appealing results for wide-range image blending, but also able to provide superior performance with respect to several baselines built upon the state-of-the-art image inpainting and outpainting approaches.

原文English
主出版物標題Proceedings - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021
發行者IEEE Computer Society
頁面843-851
頁數9
ISBN(電子)9781665445092
DOIs
出版狀態Published - 2021
事件2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021 - Virtual, Online, United States
持續時間: 19 6月 202125 6月 2021

出版系列

名字Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN(列印)1063-6919

Conference

Conference2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021
國家/地區United States
城市Virtual, Online
期間19/06/2125/06/21

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