{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,19]],"date-time":"2026-07-19T09:44:25Z","timestamp":1784454265115,"version":"3.55.0"},"reference-count":39,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2023,8,8]],"date-time":"2023-08-08T00:00:00Z","timestamp":1691452800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Innovation and entrepreneurship training program for college students of China"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s23167026","type":"journal-article","created":{"date-parts":[[2023,8,8]],"date-time":"2023-08-08T12:45:53Z","timestamp":1691498753000},"page":"7026","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Multi-Patch Hierarchical Transmission Channel Image Dehazing Network Based on Dual Attention Level Feature Fusion"],"prefix":"10.3390","volume":"23","author":[{"given":"Wenjiao","family":"Zai","sequence":"first","affiliation":[{"name":"College of Engineering, Sichuan Normal University, Chengdu 610101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-1205-7289","authenticated-orcid":false,"given":"Lisha","family":"Yan","sequence":"additional","affiliation":[{"name":"College of Engineering, Sichuan Normal University, Chengdu 610101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"713634","DOI":"10.3389\/fenrg.2021.713634","article-title":"Unmanned aerial vehicle for transmission line inspection: Status, standardization, and perspectives","volume":"9","author":"Li","year":"2021","journal-title":"Front. Energy Res."},{"key":"ref_2","first-page":"70","article-title":"Genesis and dissipation mechanisms of radiation-advection fogs in Chengdu based on multiple detection data","volume":"47","author":"Zhang","year":"2019","journal-title":"Meteorol. Sci. Technol."},{"key":"ref_3","first-page":"55","article-title":"On restoration of mountain haze image based on non-local prior algorithm","volume":"29","author":"Zhao","year":"2022","journal-title":"Electron. Opt. Control"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Imran, A., Zhu, Q., Sulaman, M., Bukhtiar, A., and Xu, M. (2023). Electric-Dipole Gated Two Terminal Phototransistor for Charge-Coupled Device. Adv. Opt. Mater., 2300910.","DOI":"10.1002\/adom.202300910"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1021\/ed039p333","article-title":"The beer-lambert law","volume":"39","author":"Swinehart","year":"1962","journal-title":"J. Chem. Educ."},{"key":"ref_6","first-page":"2341","article-title":"Single image haze removal using dark channel prior","volume":"33","author":"He","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3522","DOI":"10.1109\/TIP.2015.2446191","article-title":"A fast single image haze removal algorithm using color attenuation prior","volume":"24","author":"Zhu","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"5187","DOI":"10.1109\/TIP.2016.2598681","article-title":"DehazeNet: An end-to-end system for single image haze removal","volume":"25","author":"Cai","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Li, B., Peng, X., Wang, Z., Xu, J., and Feng, D. (2017, January 22\u201329). AOD-Net: All-in-one dehazing network. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.511"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhang, H., and Patel, V.-M. (2018, January 18\u201322). Densely connected pyramid dehazing network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00337"},{"key":"ref_11","unstructured":"Li, Y., Miao, Q., Quyang, W., Ma, Z., Fang, H., Dong, C., and Quan, Y. (November, January 27). LAP-Net: Level-aware progressive network for image dehazing. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"6523","DOI":"10.1109\/TIP.2020.2991509","article-title":"Task-oriented network for image dehazing","volume":"29","author":"Li","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1217","DOI":"10.1109\/TIP.2022.3140609","article-title":"Self-guided image dehazing using progressive feature fusion","volume":"31","author":"Bai","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Das, S.-D., and Dutta, S. (2020, January 14\u201319). Fast deep multi-patch hierarchical network for nonhomogeneous image dehazing. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00249"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhang, H., Dai, Y., Li, H., and Koniusz, P. (2019, January 15\u201321). Deep stacked hierarchical multi-patch network for image deblurring. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00613"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"118485","DOI":"10.1109\/ACCESS.2020.3003784","article-title":"Uneven image dehazing by heterogeneous twin network","volume":"8","author":"Wang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_17","unstructured":"Liu, X., Ma, Y., Shi, Z., and Chen, J. (November, January 27). Griddehazenet: Attention-based multi-scale network for image dehazing. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Qin, X., Wang, Z., Bai, Y., Xie, X., and Jia, H. (2020, January 7\u201312). FFA-Net: Feature fusion attention network for single image dehazing. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i07.6865"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"107279","DOI":"10.1016\/j.knosys.2021.107279","article-title":"EAA-Net: A novel edge assisted attention network for single image dehazing","volume":"228","author":"Wang","year":"2021","journal-title":"Knowl.-Based Syst."},{"key":"ref_20","first-page":"250","article-title":"Multi-patch and multi-scale hierarchical aggregation network for fast nonhomogeneous image dehazing","volume":"48","author":"Yang","year":"2021","journal-title":"Comput. Sci."},{"key":"ref_21","first-page":"575","article-title":"Uneven hazy image dehazing based on transmitted attention mechanism","volume":"35","author":"Wang","year":"2022","journal-title":"Pattern Recognit. Artif. Intell."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhao, D., Mo, B., Zhu, X., Zhao, J., Zhang, H., Tao, Y., and Zhao, C. (2023). Dynamic Multi-Attention Dehazing Network with Adaptive Feature Fusion. Electronics, 12.","DOI":"10.3390\/electronics12030529"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Guo, Y., Gao, Y., Liu, W., Lu, Y., Qu, J., He, S., and Ren, W. (2023, January 18\u201322). SCANet: Self-Paced Semi-Curricular Attention Network for Non-Homogeneous Image Dehazing. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPRW59228.2023.00186"},{"key":"ref_24","first-page":"1","article-title":"Image dehazing method of transmission line for unmanned aerial vehicle inspection based on densely connection pyramid network","volume":"2020","author":"Liu","year":"2020","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_25","first-page":"299","article-title":"Study on the enhancement method of online monitoring image of dense fog environment with power lines in smart city","volume":"16","author":"Zhang","year":"2022","journal-title":"Front. Neurorobotics"},{"key":"ref_26","first-page":"89","article-title":"Dark channel prior dehazing method for transmission channel image based on sky region segmentation","volume":"48","author":"Zhai","year":"2021","journal-title":"J. North China Electr. Power Univ."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Xin, R., Chen, X., Wu, J., Yang, K., Wang, X., and Zhai, Y. (2023). Insulator Umbrella Disc Shedding Detection in Foggy Weather. Sensors, 22.","DOI":"10.3390\/s22134871"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Gao, Y., Yang, J., Zhang, K., Peng, H., Wang, Y., Xia, N., and Yao, G. (2022). A New Method of Conductor Galloping Monitoring Using the Target Detection of Infrared Source. Electronics, 11.","DOI":"10.3390\/electronics11081207"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Yan, L., Zai, W., Wang, J., and Yang, D. (2023, January 27\u201330). Image Defogging Method for Transmission Channel Inspection by UAV Based on Deep Multi-patch Layered Network. Proceedings of the Panda Forum on Power and Energy (PandaFPE), Chengdu, China.","DOI":"10.1109\/PandaFPE57779.2023.10140656"},{"key":"ref_30","first-page":"217","article-title":"Review of hazy image sharpening methods","volume":"18","author":"Wang","year":"2023","journal-title":"CAAI Trans. Telligent Syst."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ancuti, C.-O., Ancuti, C., Sbert, D., and Timofte, R. (2019, January 22\u201325). Dense-haze: A benchmark for image dehazing with dense-haze and haze-free images. Proceedings of the 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan.","DOI":"10.1109\/ICIP.2019.8803046"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Ancuti, C.-O., Ancuti, C., Timofte, R., and De, C. (2018, January 18\u201322). O-haze: A dehazing benchmark with real hazy and haze-free outdoor images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00119"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ancuti, C.-O., Ancuti, C., and Timofte, R. (2020, January 14\u201319). NH-HAZE: An image dehazing benchmark with non-homogeneous hazy and haze-free images. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00230"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhang, N., Zhang, L., and Cheng, Z. (2017, January 14\u201318). Towards simulating foggy and hazy images and evaluating their authenticity. Proceedings of the Neural Information Processing: 24th International Conference, Guangzhou, China.","DOI":"10.1007\/978-3-319-70090-8_42"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Hars\u00e1nyi, K., Kiss, K., Majdik, A., and Sziranyi, T. (2019, January 1). A hybrid CNN approach for single image depth estimation: A case study. Proceedings of the International Conference on Multimedia and Network Information System, Hong Kong, China.","DOI":"10.1007\/978-3-319-98678-4_38"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1109\/MSP.2008.930649","article-title":"Mean squared error: Love it or leave it? A new look at signal fidelity measures","volume":"26","author":"Wang","year":"2009","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: From error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Venkatanath, N., Praneeth, D., Bh, M.-C., Channappayya, S., and Medasani, S. (March, January 27). Blind image quality evaluation using perception based features. Proceedings of the 2015 Twenty First National Conference on Communications (NCC), Munbai, India.","DOI":"10.1109\/NCC.2015.7084843"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"106208","DOI":"10.1016\/j.compbiomed.2022.106208","article-title":"Multi-task multi-scale learning for outcome prediction in 3D PET images","volume":"151","author":"Amyar","year":"2022","journal-title":"Comput. Biol. Med."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/16\/7026\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:28:02Z","timestamp":1760128082000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/16\/7026"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,8]]},"references-count":39,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["s23167026"],"URL":"https:\/\/doi.org\/10.3390\/s23167026","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,8]]}}}