{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T00:00:04Z","timestamp":1780099204033,"version":"3.54.0"},"reference-count":33,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,6,9]],"date-time":"2024-06-09T00:00:00Z","timestamp":1717891200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100010418","name":"MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center)","doi-asserted-by":"publisher","award":["IITP-2024-RS-2022-00156354"],"award-info":[{"award-number":["IITP-2024-RS-2022-00156354"]}],"id":[{"id":"10.13039\/501100010418","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010418","name":"MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center)","doi-asserted-by":"publisher","award":["R2022020059"],"award-info":[{"award-number":["R2022020059"]}],"id":[{"id":"10.13039\/501100010418","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006465","name":"Korea Creative Content Agency","doi-asserted-by":"publisher","award":["IITP-2024-RS-2022-00156354"],"award-info":[{"award-number":["IITP-2024-RS-2022-00156354"]}],"id":[{"id":"10.13039\/501100006465","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006465","name":"Korea Creative Content Agency","doi-asserted-by":"publisher","award":["R2022020059"],"award-info":[{"award-number":["R2022020059"]}],"id":[{"id":"10.13039\/501100006465","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this paper, we propose a lightweight U-net architecture neural network model based on Dark Channel Prior (DCP) for efficient haze (fog) removal with a single input. The existing DCP requires high computational complexity in its operation. These computations are challenging to accelerate, and the problem is exacerbated when dealing with high-resolution images (videos), making it very difficult to apply to general-purpose applications. Our proposed model addresses this issue by employing a two-stage neural network structure, replacing the computationally complex operations of the conventional DCP with easily accelerated convolution operations to achieve high-quality fog removal. Furthermore, our proposed model is designed with an intuitive structure using a relatively small number of parameters (2M), utilizing resources efficiently. These features demonstrate the effectiveness and efficiency of the proposed model for fog removal. The experimental results show that the proposed neural network model achieves an average Peak Signal-to-Noise Ratio (PSNR) of 26.65 dB and a Structural Similarity Index Measure (SSIM) of 0.88, indicating an improvement in the average PSNR of 11.5 dB and in SSIM of 0.22 compared to the conventional DCP. This shows that the proposed neural network achieves comparable results to CNN-based neural networks that have achieved SOTA-class performance, despite its intuitive structure with a relatively small number of parameters.<\/jats:p>","DOI":"10.3390\/s24123746","type":"journal-article","created":{"date-parts":[[2024,6,10]],"date-time":"2024-06-10T08:59:06Z","timestamp":1718009946000},"page":"3746","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Efficient Haze Removal from a Single Image Using a DCP-Based Lightweight U-Net Neural Network Model"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-6412-1133","authenticated-orcid":false,"given":"Yunho","family":"Han","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiyoung","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9170-1483","authenticated-orcid":false,"given":"Jinyoung","family":"Lee","sequence":"additional","affiliation":[{"name":"Korea Electronics Technology Institute, Seongnam 13509, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jae-Ho","family":"Nah","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Sangmyung University, Seoul 03016, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yo-Sung","family":"Ho","sequence":"additional","affiliation":[{"name":"EXARION, Seoul 05006, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Woo-Chan","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.neunet.2020.07.025","article-title":"Deep learning on image denoising: An overview","volume":"131","author":"Tian","year":"2020","journal-title":"Neural Netw."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2341","DOI":"10.1109\/TPAMI.2010.168","article-title":"Single Image Haze Removal Using Dark Channel Prior","volume":"33","author":"He","year":"2011","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_3","unstructured":"Schechner, Y.Y., Narasimhan, S.G., and Nayar, S.K. (2001, January 8\u201314). Instant dehazing of images using polarization. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, Kauai, HI, USA."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1109\/TPAMI.2003.1201821","article-title":"Contrast restoration of weather degraded images","volume":"25","author":"Narasimhan","year":"2003","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_5","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_6","doi-asserted-by":"crossref","first-page":"4449","DOI":"10.1007\/s11831-021-09541-6","article-title":"Single Image Defogging using Deep Learning Techniques: Past, Present and Future","volume":"28","author":"Sharma","year":"2021","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Huang, Y., and Chen, Y. (2020, January 11\u201314). Survey of State-of-Art Autonomous Driving Technologies with Deep Learning. Proceedings of the 2020 IEEE 20th International Conference on Software Quality, Reliability and Security Companion (QRS-C), Macau, China.","DOI":"10.1109\/QRS-C51114.2020.00045"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Sun, H., Ang, M.H., and Rus, D. (2019, January 3\u20138). A Convolutional Network for Joint Deraining and Dehazing from A Single Image for Autonomous Driving in Rain. Proceedings of the 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China.","DOI":"10.1109\/IROS40897.2019.8967644"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"7576","DOI":"10.1109\/TII.2024.3363089","article-title":"Phase Space Graph Convolutional Network for Chaotic Time Series Learning","volume":"20","author":"Ren","year":"2024","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. Lecture Notes in Computer Science, Springer International Publishing.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Guo, S., Yan, Z., Zhang, K., Zuo, W., and Zhang, L. (2019, January 15\u201320). Toward Convolutional Blind Denoising of Real Photographs. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00181"},{"key":"ref_13","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 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.511"},{"key":"ref_14","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_15","doi-asserted-by":"crossref","unstructured":"Ren, W., Liu, S., Zhang, H., Pan, J., Cao, X., and Yang, M.-H. (2016). Single Image Dehazing via Multi-scale Convolutional Neural Networks. Computer Vision-ECCV 2016, Springer International Publishing.","DOI":"10.1007\/978-3-319-46475-6_10"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wu, H., Gao, T., Ji, Z., Song, M., Zhang, L., and Kong, D. (2023). Dark-Channel Soft-Constrained and Object-Perception-Enhanced Deep Dehazing Networks Used for Road Inspection Images. Sensors, 23.","DOI":"10.3390\/s23218932"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Dong, H., Pan, J., Xiang, L., Hu, Z., and Zhang, X. (2020, January 13\u201319). Multi-Scale Boosted Dehazing Network with Dense Feature Fusion. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00223"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yang, G., Yang, H., Yu, S., Wang, J., and Nie, Z. (2023). A Multi-Scale Dehazing Network with Dark Channel Priors. Sensors, 23.","DOI":"10.3390\/s23135980"},{"key":"ref_19","first-page":"11908","article-title":"FFA-Net: Feature Fusion Attention Network for Single Image Dehazing","volume":"34","author":"Qin","year":"2020","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Shi, Z., Huo, J., Meng, Z., Yang, F., and Wang, Z. (2023). An Adversarial Dual-Branch Network for Nonhomogeneous Dehazing in Tunnel Construction. Sensors, 23.","DOI":"10.3390\/s23229245"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Chen, Z., Wang, Y., Yang, Y., and Liu, D. (2021, January 19\u201325). PSD: Principled Synthetic-to-Real Dehazing Guided by Physical Priors. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00710"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Liu, J., Wu, H., Xie, Y., Qu, Y., and Ma, L. (2020, January 14\u201319). Trident Dehazing Network. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00223"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Yu, Y., Liu, H., Fu, M., Chen, J., Wang, X., and Wang, K. (2021, January 20\u201325). A Two-branch Neural Network for Non-homogeneous Dehazing via Ensemble Learning. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Nashville, TN, USA.","DOI":"10.1109\/CVPRW53098.2021.00028"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1397","DOI":"10.1109\/TPAMI.2012.213","article-title":"Guided Image Filtering","volume":"35","author":"He","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1016\/j.jvcir.2013.12.011","article-title":"Weighted haze removal method with halo prevention","volume":"25","author":"Shiau","year":"2014","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1049\/iet-cvi.2013.0011","article-title":"Single image haze removal using content-adaptive dark channel and post enhancement","volume":"8","author":"Li","year":"2014","journal-title":"IET Comput. Vis."},{"key":"ref_27","unstructured":"OpenCV (2024, January 01). Color Space Conversions. Accessed: May 2024. [Online]. Available online: https:\/\/docs.opencv.org\/4.x\/d8\/d01\/group__imgproc__color__conversions.html."},{"key":"ref_28","unstructured":"PyTorch (2017, January 01). Transforming and Augmenting Images > Grayscale. [Online]. Available online: https:\/\/pytorch.org\/vision\/main\/generated\/torchvision.transforms.Grayscale.html."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely Connected Convolutional Networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1109\/TCI.2016.2644865","article-title":"Loss Functions for Image Restoration with Neural Networks","volume":"3","author":"Zhao","year":"2017","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_32","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":"2019","journal-title":"IEEE Trans. Image Process."},{"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 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00230"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/12\/3746\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:56:03Z","timestamp":1760108163000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/12\/3746"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,9]]},"references-count":33,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["s24123746"],"URL":"https:\/\/doi.org\/10.3390\/s24123746","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,9]]}}}