Multi-Patch Hierarchical Transmission Channel Image Dehazing Network Based on Dual Attention Level Feature Fusion
Unmanned Aerial Vehicle (UAV) inspection of transmission channels in mountainous areas is susceptible to non-homogeneous fog, such as up-slope fog and advection fog, which causes crucial portions of transmission lines or towers to become fuzzy or even wholly concealed. This paper presents a Dual Att...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/16/7026 |
| _version_ | 1797583250443468800 |
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| author | Wenjiao Zai Lisha Yan |
| author_facet | Wenjiao Zai Lisha Yan |
| author_sort | Wenjiao Zai |
| collection | DOAJ |
| description | Unmanned Aerial Vehicle (UAV) inspection of transmission channels in mountainous areas is susceptible to non-homogeneous fog, such as up-slope fog and advection fog, which causes crucial portions of transmission lines or towers to become fuzzy or even wholly concealed. This paper presents a Dual Attention Level Feature Fusion Multi-Patch Hierarchical Network (DAMPHN) for single image defogging to address the bad quality of cross-level feature fusion in Fast Deep Multi-Patch Hierarchical Networks (FDMPHN). Compared with FDMPHN before improvement, the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) of DAMPHN are increased by 0.3 dB and 0.011 on average, and the Average Processing Time (APT) of a single picture is shortened by 11%. Additionally, compared with the other three excellent defogging methods, the PSNR and SSIM values DAMPHN are increased by 1.75 dB and 0.022 on average. Then, to mimic non-homogeneous fog, we combine the single picture depth information with 3D Berlin noise to create the UAV-HAZE dataset, which is used in the field of UAV power assessment. The experiment demonstrates that DAMPHN offers excellent defogging results and is competitive in no-reference and full-reference assessment indices. |
| first_indexed | 2024-03-10T23:36:12Z |
| format | Article |
| id | doaj.art-0041ce28c45f4f92b5f9398a4a0673e8 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T23:36:12Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-0041ce28c45f4f92b5f9398a4a0673e82023-11-19T02:55:44ZengMDPI AGSensors1424-82202023-08-012316702610.3390/s23167026Multi-Patch Hierarchical Transmission Channel Image Dehazing Network Based on Dual Attention Level Feature FusionWenjiao Zai0Lisha Yan1College of Engineering, Sichuan Normal University, Chengdu 610101, ChinaCollege of Engineering, Sichuan Normal University, Chengdu 610101, ChinaUnmanned Aerial Vehicle (UAV) inspection of transmission channels in mountainous areas is susceptible to non-homogeneous fog, such as up-slope fog and advection fog, which causes crucial portions of transmission lines or towers to become fuzzy or even wholly concealed. This paper presents a Dual Attention Level Feature Fusion Multi-Patch Hierarchical Network (DAMPHN) for single image defogging to address the bad quality of cross-level feature fusion in Fast Deep Multi-Patch Hierarchical Networks (FDMPHN). Compared with FDMPHN before improvement, the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) of DAMPHN are increased by 0.3 dB and 0.011 on average, and the Average Processing Time (APT) of a single picture is shortened by 11%. Additionally, compared with the other three excellent defogging methods, the PSNR and SSIM values DAMPHN are increased by 1.75 dB and 0.022 on average. Then, to mimic non-homogeneous fog, we combine the single picture depth information with 3D Berlin noise to create the UAV-HAZE dataset, which is used in the field of UAV power assessment. The experiment demonstrates that DAMPHN offers excellent defogging results and is competitive in no-reference and full-reference assessment indices.https://www.mdpi.com/1424-8220/23/16/7026transmission channelsnon-homogeneous fogdual attentionDAMPHNimage defogging |
| spellingShingle | Wenjiao Zai Lisha Yan Multi-Patch Hierarchical Transmission Channel Image Dehazing Network Based on Dual Attention Level Feature Fusion Sensors transmission channels non-homogeneous fog dual attention DAMPHN image defogging |
| title | Multi-Patch Hierarchical Transmission Channel Image Dehazing Network Based on Dual Attention Level Feature Fusion |
| title_full | Multi-Patch Hierarchical Transmission Channel Image Dehazing Network Based on Dual Attention Level Feature Fusion |
| title_fullStr | Multi-Patch Hierarchical Transmission Channel Image Dehazing Network Based on Dual Attention Level Feature Fusion |
| title_full_unstemmed | Multi-Patch Hierarchical Transmission Channel Image Dehazing Network Based on Dual Attention Level Feature Fusion |
| title_short | Multi-Patch Hierarchical Transmission Channel Image Dehazing Network Based on Dual Attention Level Feature Fusion |
| title_sort | multi patch hierarchical transmission channel image dehazing network based on dual attention level feature fusion |
| topic | transmission channels non-homogeneous fog dual attention DAMPHN image defogging |
| url | https://www.mdpi.com/1424-8220/23/16/7026 |
| work_keys_str_mv | AT wenjiaozai multipatchhierarchicaltransmissionchannelimagedehazingnetworkbasedondualattentionlevelfeaturefusion AT lishayan multipatchhierarchicaltransmissionchannelimagedehazingnetworkbasedondualattentionlevelfeaturefusion |