{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T01:33:03Z","timestamp":1780363983646,"version":"3.54.1"},"reference-count":28,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2023,6,27]],"date-time":"2023-06-27T00:00:00Z","timestamp":1687824000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Jiangxi Provincial Department of Education","award":["GJJ190450"],"award-info":[{"award-number":["GJJ190450"]}]},{"name":"Jiangxi Provincial Department of Education","award":["GJJ180484"],"award-info":[{"award-number":["GJJ180484"]}]},{"name":"Jiangxi Provincial Department of Education","award":["GJJ190450"],"award-info":[{"award-number":["GJJ190450"]}]},{"name":"Jiangxi Provincial Department of Education","award":["GJJ180484"],"award-info":[{"award-number":["GJJ180484"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Image dehazing based on convolutional neural networks has achieved significant success; however, there are still some problems, such as incomplete dehazing, color deviation, and loss of detailed information. To address these issues, in this study, we propose a multi-scale dehazing network with dark channel priors (MSDN-DCP). First, we introduce a feature extraction module (FEM), which effectively enhances the ability of feature extraction and correlation through a two-branch residual structure. Second, a feature fusion module (FFM) is devised to combine multi-scale features adaptively at different stages. Finally, we propose a dark channel refinement module (DCRM) that implements the dark channel prior theory to guide the network in learning the features of the hazy region, ultimately refining the feature map that the network extracted. We conduct experiments using the Haze4K dataset, and the achieved results include a peak signal-to-noise ratio of 29.57 dB and a structural similarity of 98.1%. The experimental results show that the MSDN-DCP can achieve superior dehazing compared to other algorithms in terms of objective metrics and visual perception.<\/jats:p>","DOI":"10.3390\/s23135980","type":"journal-article","created":{"date-parts":[[2023,6,28]],"date-time":"2023-06-28T00:45:11Z","timestamp":1687913111000},"page":"5980","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["A Multi-Scale Dehazing Network with Dark Channel Priors"],"prefix":"10.3390","volume":"23","author":[{"given":"Guoliang","family":"Yang","sequence":"first","affiliation":[{"name":"School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou 341000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou 341000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuaiying","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou 341000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jixiang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou 341000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziling","family":"Nie","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou 341000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wang, D., and Wang, Z. (2022, January 23\u201325). Research and Implementation of Image Dehazing Based on Deep Learning. Proceedings of the 2022 International Conference on Computer Network, Electronic and Automation (ICCNEA), Xi\u2019an, China.","DOI":"10.1109\/ICCNEA57056.2022.00020"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1007\/s42979-021-00640-6","article-title":"Autonomous bot using machine learning and computer vision","volume":"2","author":"Karkera","year":"2021","journal-title":"SN Comput. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Luo, Y., Wei, H., Li, Y., Qi, G., Mazur, N., Li, Y., and Li, P. (2021). Atmospheric light estimation based remote sensing image dehazing. Remote Sens., 13.","DOI":"10.3390\/rs13132432"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"6999","DOI":"10.1109\/TNNLS.2021.3084827","article-title":"A survey of convolutional neural networks: Analysis, applications, and prospects","volume":"33","author":"Li","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wu, H., Qu, Y., Lin, S., Zhou, J., Qiao, R., Zhang, Z., Xie, Y., and Ma, L. (2021, January 20\u201325). Contrastive learning for compact single image dehazing. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01041"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ding, X., Zhang, X., Han, J., and Ding, G. (2022, January 18\u201324). Scaling up your kernels to 31\u00d731: Revisiting large kernel design in cnns. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01166"},{"key":"ref_7","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany. Proceedings, Part III 18."},{"key":"ref_8","unstructured":"Narasimhan, S.G., and Nayar, S.K. (2020, January 15). Chromatic framework for vision in bad weather. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Hilton Head, SC, USA. CVPR 2000 (Cat. No. PR00662)."},{"key":"ref_9","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_10","doi-asserted-by":"crossref","first-page":"1397","DOI":"10.1109\/TPAMI.2012.213","article-title":"Guided image filtering","volume":"35","author":"He","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1109\/LSP.2017.2780886","article-title":"Single image dehazing based on dark channel prior and energy minimization","volume":"25","author":"Zhu","year":"2017","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1405","DOI":"10.1109\/LSP.2020.3013741","article-title":"Haze removal: Push DCP at the edge","volume":"27","author":"Yang","year":"2020","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_13","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_14","doi-asserted-by":"crossref","unstructured":"Zhang, H., and Patel, V.M. (2018, January 18\u201323). 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_15","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_16","doi-asserted-by":"crossref","unstructured":"Wang, D., Zhang, Y., Wan, Z., Gu, F., Chen, M., Zhou, Y., Zhang, Y., and Zhu, Y. (2022, January 16\u201319). A Novel Approach for Image Dehazing via Spatial and Channel Feature Fusion. Proceedings of the 2022 8th Annual International Conference on Network and Information Systems for Computers (ICNISC), Hangzhou, China.","DOI":"10.1109\/ICNISC57059.2022.00070"},{"key":"ref_17","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_18","doi-asserted-by":"crossref","unstructured":"Xu, H., Ma, J., Le, Z., Jiang, J., and Guo, X. (2020, January 7\u201312). Fusiondn: A unified densely connected network for image fusion. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i07.6936"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4668001","DOI":"10.1155\/2022\/4668001","article-title":"An extremely effective spatial pyramid and pixel shuffle upsampling decoder for multiscale monocular depth estimation","volume":"2022","author":"Luo","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Li, X., Wang, W., Hu, X., and Yang, J. (2019, January 15\u201320). Selective kernel networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00060"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhu, L., Pei, S., Fu, H., Qin, J., Zhang, Q., Wan, L., and Feng, W. (2021, January 20\u201324). From synthetic to real: Image dehazing collaborating with unlabeled real data. Proceedings of the 29th ACM International Conference on Multimedia, Chengdu, China.","DOI":"10.1145\/3474085.3475331"},{"key":"ref_22","first-page":"746","article-title":"Indoor segmentation and support inference from rgbd images","volume":"Volume 7576","author":"Silberman","year":"2012","journal-title":"ECCV (5)"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1109\/TIP.2018.2867951","article-title":"Benchmarking single-image dehazing and beyond","volume":"28","author":"Li","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"6947","DOI":"10.1109\/TIP.2020.2995264","article-title":"Dehazing evaluation: Real-world benchmark datasets, criteria, and baselines","volume":"29","author":"Zhao","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wang, C., Fan, H., Zhang, H., and Li, Z. (2019, January 16\u201318). Pixel-Level dehazed image quality assessment based on dark channel prior and depth. Proceedings of the 2019 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA\/BDCloud\/SocialCom\/SustainCom), Xiamen, China.","DOI":"10.1109\/ISPA-BDCloud-SustainCom-SocialCom48970.2019.00227"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Dong, H., Pan, J., Xiang, L., Hu, Z., Zhang, X., Wang, F., and Yang, M.-H. (2020, January 15). Multi-scale boosted dehazing network with dense feature fusion. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Hilton Head, SC, USA.","DOI":"10.1109\/CVPR42600.2020.00223"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Tu, Z., Talebi, H., Zhang, H., Yang, F., Milanfar, P., Bovik, A., and Li, Y. (2022, January 18\u201324). Maxim: Multi-axis mlp for image processing. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00568"},{"key":"ref_28","unstructured":"Chen, Z., He, Z., and Lu, Z.M. (2023). DEA-Net: Single image dehazing based on detail-enhanced convolution and content-guided attention. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/13\/5980\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:02:10Z","timestamp":1760126530000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/13\/5980"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,27]]},"references-count":28,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["s23135980"],"URL":"https:\/\/doi.org\/10.3390\/s23135980","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,27]]}}}