Deep blur detection network with boundary-aware multi-scale features
Recently, blur detection is a hot topic in computer vision. It can accurately segment the blurred areas from an image, which is conducive for the post-processing of the image. Although many hand-crafted features based approaches have been presented during the last decades, they were not robust to th...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2022-12-01
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| Series: | Connection Science |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/09540091.2021.1933906 |
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| _version_ | 1849738721736261632 |
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| author | Xiaoli Sun Qiwei Wang Xiujun Zhang Chen Xu Weiqiang Zhang |
| author_facet | Xiaoli Sun Qiwei Wang Xiujun Zhang Chen Xu Weiqiang Zhang |
| author_sort | Xiaoli Sun |
| collection | DOAJ |
| description | Recently, blur detection is a hot topic in computer vision. It can accurately segment the blurred areas from an image, which is conducive for the post-processing of the image. Although many hand-crafted features based approaches have been presented during the last decades, they were not robust to the complex scenarios. To solve this problem, we newly establish a boundary-aware multi-scale deep network in this paper. First, the VGG-16 network is used to extract the deep features from multi-scale layers. Contrast layers and deconvolutional layers are added to make the difference between the blurred areas and clear areas more prominent. At last, a new boundary-aware penalty is introduced, which makes the edges of our results much clearer. Our method spends about 0.2 s to evaluate an image. Experiments on the large dataset confirm that the proposed model performs better than other models. |
| format | Article |
| id | doaj-art-ef5a5fec5bbc4a7c863ef30801d2a735 |
| institution | DOAJ |
| issn | 0954-0091 1360-0494 |
| language | English |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj-art-ef5a5fec5bbc4a7c863ef30801d2a7352025-08-20T03:06:28ZengTaylor & Francis GroupConnection Science0954-00911360-04942022-12-0134176678410.1080/09540091.2021.19339061933906Deep blur detection network with boundary-aware multi-scale featuresXiaoli Sun0Qiwei Wang1Xiujun Zhang2Chen Xu3Weiqiang Zhang4Shenzhen UniversityShenzhen UniversitySchool of Electronic and Communication EngineeringShenzhen UniversityShenzhen UniversityRecently, blur detection is a hot topic in computer vision. It can accurately segment the blurred areas from an image, which is conducive for the post-processing of the image. Although many hand-crafted features based approaches have been presented during the last decades, they were not robust to the complex scenarios. To solve this problem, we newly establish a boundary-aware multi-scale deep network in this paper. First, the VGG-16 network is used to extract the deep features from multi-scale layers. Contrast layers and deconvolutional layers are added to make the difference between the blurred areas and clear areas more prominent. At last, a new boundary-aware penalty is introduced, which makes the edges of our results much clearer. Our method spends about 0.2 s to evaluate an image. Experiments on the large dataset confirm that the proposed model performs better than other models.http://dx.doi.org/10.1080/09540091.2021.1933906blur detectionmulti-scaleboundary-awaredeep learning |
| spellingShingle | Xiaoli Sun Qiwei Wang Xiujun Zhang Chen Xu Weiqiang Zhang Deep blur detection network with boundary-aware multi-scale features Connection Science blur detection multi-scale boundary-aware deep learning |
| title | Deep blur detection network with boundary-aware multi-scale features |
| title_full | Deep blur detection network with boundary-aware multi-scale features |
| title_fullStr | Deep blur detection network with boundary-aware multi-scale features |
| title_full_unstemmed | Deep blur detection network with boundary-aware multi-scale features |
| title_short | Deep blur detection network with boundary-aware multi-scale features |
| title_sort | deep blur detection network with boundary aware multi scale features |
| topic | blur detection multi-scale boundary-aware deep learning |
| url | http://dx.doi.org/10.1080/09540091.2021.1933906 |
| work_keys_str_mv | AT xiaolisun deepblurdetectionnetworkwithboundaryawaremultiscalefeatures AT qiweiwang deepblurdetectionnetworkwithboundaryawaremultiscalefeatures AT xiujunzhang deepblurdetectionnetworkwithboundaryawaremultiscalefeatures AT chenxu deepblurdetectionnetworkwithboundaryawaremultiscalefeatures AT weiqiangzhang deepblurdetectionnetworkwithboundaryawaremultiscalefeatures |