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For the dehazing network, a multi-layer feature fusion module is used in the generator to fuse the features of different coding layers of U-Net to enhance the network\u2019s recovery of information such as details and edges, and a frequency domain channel attention mechanism is added at the key nodes of the network to enhance the network\u2019s attention to different fog concentrations. At the same time, to improve the discriminant effect of the discriminator, the discriminator is extended to a global and local discriminator. The experimental results show that the dehaze effect on Reside and other test data sets is better than the comparison method. The peak signal-to-noise ratio is improved by 2.26\u00a0dB compared to the highest GCA-Net algorithm. According to the lane detection of fog images, it is found that the proposed network improves the accuracy of lane detection on foggy days.<\/jats:p>","DOI":"10.3233\/aic-230227","type":"journal-article","created":{"date-parts":[[2024,5,31]],"date-time":"2024-05-31T10:53:36Z","timestamp":1717152816000},"page":"619-635","source":"Crossref","is-referenced-by-count":0,"title":["Multi-feature fusion dehazing based on CycleGAN"],"prefix":"10.1177","volume":"37","author":[{"given":"Jingpin","family":"Wang","sequence":"first","affiliation":[{"name":"School of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuan","family":"Ge","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chao","family":"Han","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/AIC-230227_ref1","doi-asserted-by":"crossref","unstructured":"C.O.\u00a0Ancuti, C.\u00a0Ancuti, M.\u00a0Sbert et al., Dense-haze: A benchmark for image dehazing with densehaze and haze-free images, in: Proceeding of the IEEE International Conference on Image Processing(ICIP), 2019, pp.\u00a01014\u20131018.","DOI":"10.1109\/ICIP.2019.8803046"},{"key":"10.3233\/AIC-230227_ref2","doi-asserted-by":"crossref","unstructured":"C.O.\u00a0Ancuti, C.\u00a0Ancuti and R.\u00a0Timofte, NH-HAZE: An image dehazing benchmark with non-homogeneous hazy and haze-free images, in: Proceeding of the IEEE\/CVE Conference on 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