Multi-Step Structure Image Inpainting Model with Attention Mechanism
The proliferation of deep learning has propelled image inpainting to an important research field. Although the current image inpainting model has made remarkable achievements, the two-stage image inpainting method is easy to produce structural errors in the rough stage because of insufficient treatm...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/4/2316 |
| _version_ | 1797618248200486912 |
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| author | Cai Ran Xinfu Li Fang Yang |
| author_facet | Cai Ran Xinfu Li Fang Yang |
| author_sort | Cai Ran |
| collection | DOAJ |
| description | The proliferation of deep learning has propelled image inpainting to an important research field. Although the current image inpainting model has made remarkable achievements, the two-stage image inpainting method is easy to produce structural errors in the rough stage because of insufficient treatment of the rough inpainting stage. To address this problem, we propose a multi-step structured image inpainting model combining attention mechanisms. Different from the previous two-stage inpainting model, we divide the damaged area into four sub-areas, calculate the priority of each area according to the priority, specify the inpainting order, and complete the rough inpainting stage several times. The stability of the model is enhanced by the multi-step method. The structural attention mechanism strengthens the expression of structural features and improves the quality of structure and contour reconstruction. Experimental evaluation of benchmark data sets shows that our method effectively reduces structural errors and improves the effect of image inpainting. |
| first_indexed | 2024-03-11T08:10:23Z |
| format | Article |
| id | doaj.art-f73b4b22fed84de998518ef910a88de1 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T08:10:23Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-f73b4b22fed84de998518ef910a88de12023-11-16T23:13:12ZengMDPI AGSensors1424-82202023-02-01234231610.3390/s23042316Multi-Step Structure Image Inpainting Model with Attention MechanismCai Ran0Xinfu Li1Fang Yang2School of Cyber Security and Computer, Hebei University, Baoding 071002, ChinaSchool of Cyber Security and Computer, Hebei University, Baoding 071002, ChinaSchool of Cyber Security and Computer, Hebei University, Baoding 071002, ChinaThe proliferation of deep learning has propelled image inpainting to an important research field. Although the current image inpainting model has made remarkable achievements, the two-stage image inpainting method is easy to produce structural errors in the rough stage because of insufficient treatment of the rough inpainting stage. To address this problem, we propose a multi-step structured image inpainting model combining attention mechanisms. Different from the previous two-stage inpainting model, we divide the damaged area into four sub-areas, calculate the priority of each area according to the priority, specify the inpainting order, and complete the rough inpainting stage several times. The stability of the model is enhanced by the multi-step method. The structural attention mechanism strengthens the expression of structural features and improves the quality of structure and contour reconstruction. Experimental evaluation of benchmark data sets shows that our method effectively reduces structural errors and improves the effect of image inpainting.https://www.mdpi.com/1424-8220/23/4/2316image inpaintingimage reconstructiongenerative adversarial networksdeep learning |
| spellingShingle | Cai Ran Xinfu Li Fang Yang Multi-Step Structure Image Inpainting Model with Attention Mechanism Sensors image inpainting image reconstruction generative adversarial networks deep learning |
| title | Multi-Step Structure Image Inpainting Model with Attention Mechanism |
| title_full | Multi-Step Structure Image Inpainting Model with Attention Mechanism |
| title_fullStr | Multi-Step Structure Image Inpainting Model with Attention Mechanism |
| title_full_unstemmed | Multi-Step Structure Image Inpainting Model with Attention Mechanism |
| title_short | Multi-Step Structure Image Inpainting Model with Attention Mechanism |
| title_sort | multi step structure image inpainting model with attention mechanism |
| topic | image inpainting image reconstruction generative adversarial networks deep learning |
| url | https://www.mdpi.com/1424-8220/23/4/2316 |
| work_keys_str_mv | AT cairan multistepstructureimageinpaintingmodelwithattentionmechanism AT xinfuli multistepstructureimageinpaintingmodelwithattentionmechanism AT fangyang multistepstructureimageinpaintingmodelwithattentionmechanism |