{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T12:23:50Z","timestamp":1784291030998,"version":"3.55.0"},"reference-count":57,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,9,11]],"date-time":"2023-09-11T00:00:00Z","timestamp":1694390400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Image dehazing, a fundamental problem in computer vision, involves the recovery of clear visual cues from images marred by haze. Over recent years, deploying deep learning paradigms has spurred significant strides in image dehazing tasks. However, many dehazing networks aim to enhance performance by adopting intricate network architectures, complicating training, inference, and deployment procedures. This study proposes an end-to-end U-Net dehazing network model with recursive gated convolution and attention mechanisms to improve performance while maintaining a lean network structure. In our approach, we leverage an improved recursive gated convolution mechanism to substitute the original U-Net\u2019s convolution blocks with residual blocks and apply the SK fusion module to revamp the skip connection method. We designate this novel U-Net variant as the Dehaze Recursive Gated U-Net (DRGNet). Comprehensive testing across public datasets demonstrates the DRGNet\u2019s superior performance in dehazing quality, detail retrieval, and objective evaluation metrics. Ablation studies further confirm the effectiveness of the key design elements.<\/jats:p>","DOI":"10.3390\/jimaging9090183","type":"journal-article","created":{"date-parts":[[2023,9,11]],"date-time":"2023-09-11T09:05:17Z","timestamp":1694423117000},"page":"183","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Efficient Dehazing with Recursive Gated Convolution in U-Net: A Novel Approach for Image Dehazing"],"prefix":"10.3390","volume":"9","author":[{"given":"Zhibo","family":"Wang","sequence":"first","affiliation":[{"name":"Graduate School of Artificial Intelligence, Jeonju University, Jeonju-si 55069, Republic of Korea"},{"name":"Artificial Intelligence Research Center, Jeonju University, Jeonju-si 55069, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jia","family":"Jia","sequence":"additional","affiliation":[{"name":"Graduate School of Artificial Intelligence, Jeonju University, Jeonju-si 55069, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Lyu","sequence":"additional","affiliation":[{"name":"Graduate School of Artificial Intelligence, Jeonju University, Jeonju-si 55069, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9602-678X","authenticated-orcid":false,"given":"Jeongik","family":"Min","sequence":"additional","affiliation":[{"name":"Graduate School of Artificial Intelligence, Jeonju University, Jeonju-si 55069, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,11]]},"reference":[{"key":"ref_1","unstructured":"McCartney, E.J. 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