دورية أكاديمية
Image inpainting network based on multi‐level attention mechanism
| العنوان: | Image inpainting network based on multi‐level attention mechanism |
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| المؤلفون: | Hongyue Xiang, Weidong Min, Zitai Wei, Meng Zhu, Mengxue Liu, Ziyang Deng |
| المصدر: | IET Image Processing, Vol 18, Iss 2, Pp 428-438 (2024) |
| بيانات النشر: | Wiley, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Computer software |
| مصطلحات موضوعية: | image processing, image restoration, Image inpainting, vanilla convolution, gated convolution, multi‐level attention mechanism, Photography, TR1-1050, Computer software, QA76.75-76.765 |
| الوصف: | Abstract Image inpainting networks based on deep learning techniques have been widely used in many important fields. However, most inpainting networks fail to generate desirable repaired images. This may be due to their failure to extract effective features and accurately assign high weights to the undamaged regions. To alleviate these problems, an image inpainting network based on gated convolution and multi‐level attention mechanism (IIN‐GCMAM) is proposed in this paper. This network follows encoder–decoder architecture, consisting of the gated convolution encoder (GC‐encoder) and the multi‐level attention mechanism decoder (MAM‐decoder). The GC‐encoder weighs the extracted features with gated convolutions, which reduces the interference caused by the damaged regions. The multi‐level attention mechanism employed in the MAM‐decoder uses multi‐scale feature maps spatially and channel‐wise to improve the consistency in global structure and the fineness of repaired results. Extensive experiments are conducted on the common datasets, Paris StreetView and CelebA. Experimental results indicate that the proposed IIN‐GCMAM can achieve a good performance on the common evaluation metrics and visual effects. It can achieve 0.0408, 0.720, and 22.27 in MAE, SSIM, and PSNR at the mask ratio of 50%–60%, respectively. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1751-9667 1751-9659 |
| العلاقة: | https://doaj.org/toc/1751-9659Test; https://doaj.org/toc/1751-9667Test |
| DOI: | 10.1049/ipr2.12958 |
| الوصول الحر: | https://doaj.org/article/8789288d2bd542d9a75d03ff5a18e4d6Test |
| رقم الانضمام: | edsdoj.8789288d2bd542d9a75d03ff5a18e4d6 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 17519667 17519659 |
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| DOI: | 10.1049/ipr2.12958 |