{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T21:38:09Z","timestamp":1777757889718,"version":"3.51.4"},"reference-count":57,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,7,13]],"date-time":"2025-07-13T00:00:00Z","timestamp":1752364800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62066041"],"award-info":[{"award-number":["62066041"]}]},{"name":"National Natural Science Foundation of China","award":["202102010101008"],"award-info":[{"award-number":["202102010101008"]}]},{"name":"National Natural Science Foundation of China","award":["2024L295"],"award-info":[{"award-number":["2024L295"]}]},{"name":"Shanxi Provincial Key Research and Development Program of China","award":["62066041"],"award-info":[{"award-number":["62066041"]}]},{"name":"Shanxi Provincial Key Research and Development Program of China","award":["202102010101008"],"award-info":[{"award-number":["202102010101008"]}]},{"name":"Shanxi Provincial Key Research and Development Program of China","award":["2024L295"],"award-info":[{"award-number":["2024L295"]}]},{"name":"Scientific and Technological Innovation Programs of Higher Education Institutions","award":["62066041"],"award-info":[{"award-number":["62066041"]}]},{"name":"Scientific and Technological Innovation Programs of Higher Education Institutions","award":["202102010101008"],"award-info":[{"award-number":["202102010101008"]}]},{"name":"Scientific and Technological Innovation Programs of Higher Education Institutions","award":["2024L295"],"award-info":[{"award-number":["2024L295"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Image dehazing technology is a crucial component in the fields of intelligent transportation and autonomous driving. However, most existing dehazing algorithms only process images in the spatial domain, failing to fully exploit the rich information in the frequency domain, which leads to residual haze in the images. To address this issue, we propose a novel Frequency-domain Information Guided Symmetric Dual-branch Dehazing Network (FIGD-Net), which utilizes the spatial branch to extract local haze features and the frequency branch to capture the global haze distribution, thereby guiding the feature learning process in the spatial branch. The FIGD-Net mainly consists of three key modules: the Frequency Detail Extraction Module (FDEM), the Dual-Domain Multi-scale Feature Extraction Module (DMFEM), and the Dual-Domain Guidance Module (DGM). First, the FDEM employs the Discrete Cosine Transform (DCT) to convert the spatial domain into the frequency domain. It then selectively extracts high-frequency and low-frequency features based on predefined proportions. The high-frequency features, which contain haze-related information, are correlated with the overall characteristics of the low-frequency features to enhance the representation of haze attributes. Next, the DMFEM utilizes stacked residual blocks and gradient feature flows to capture local detail features. Specifically, frequency-guided weights are applied to adjust the focus of feature channels, thereby improving the module\u2019s ability to capture multi-scale features and distinguish haze features. Finally, the DGM adjusts channel weights guided by frequency information. This smooths out redundant signals and enables cross-branch information exchange, which helps to restore the original image colors. Extensive experiments demonstrate that the proposed FIGD-Net achieves superior dehazing performance on multiple synthetic and real-world datasets.<\/jats:p>","DOI":"10.3390\/sym17071122","type":"journal-article","created":{"date-parts":[[2025,7,14]],"date-time":"2025-07-14T07:54:23Z","timestamp":1752479663000},"page":"1122","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["FIGD-Net: A Symmetric Dual-Branch Dehazing Network Guided by Frequency Domain Information"],"prefix":"10.3390","volume":"17","author":[{"given":"Luxia","family":"Yang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Taiyuan Normal University, Jinzhong 030619, China"},{"name":"Shanxi Provincial Key Laboratory of Intelligent Optimization Computing and Blockchain Technology, Jinzhong 030619, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingzhao","family":"Xue","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Taiyuan Normal University, Jinzhong 030619, China"},{"name":"Shanxi Provincial Key Laboratory of Intelligent Optimization Computing and Blockchain Technology, Jinzhong 030619, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yijin","family":"Ning","sequence":"additional","affiliation":[{"name":"School of Software, Taiyuan University of Technology, Taiyuan 030600, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongrui","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Taiyuan Normal University, Jinzhong 030619, China"},{"name":"Shanxi Provincial Key Laboratory of Intelligent Optimization Computing and Blockchain Technology, Jinzhong 030619, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongjie","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"110763","DOI":"10.1016\/j.patcog.2024.110763","article-title":"Unsupervised multi-branch network with high-frequency enhancement for image dehazing","volume":"156","author":"Sun","year":"2024","journal-title":"Pattern Recognit."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Lv, W., Xu, S., Wei, J., Wang, G., Dang, Q., Liu, Y., and Chen, J. 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