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Most existing dehazing methods based on neural networks are inflexible and do not consider the loss in haze\u2010related feature space. They sacrificed texture details and perceptual characteristics in images. To overcome these weaknesses, we propose an image\u2010to\u2010image dehazing model based on generative adversarial networks (DHGAN) with dark channel prior. The DHGAN takes a hazy image as input and directly outputs a haze\u2010free image by applying a U\u2010net\u2010based generator. In addition to pixelwise loss and perceptual loss, we introduce dark\u2010channel\u2010minimizing loss to constrain the generated images to the manifold of natural images, thus leading to better texture details and perceptual properties. Comparative experiments on benchmark images with several state\u2010of\u2010the\u2010art dehazing methods demonstrate the effectiveness of the proposed DHGAN.<\/jats:p>","DOI":"10.1002\/cpe.5263","type":"journal-article","created":{"date-parts":[[2019,4,26]],"date-time":"2019-04-26T10:01:40Z","timestamp":1556272900000},"update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["DHGAN: Generative adversarial network with dark channel prior for single\u2010image dehazing"],"prefix":"10.1002","volume":"32","author":[{"given":"Wenxia","family":"Wu","sequence":"first","affiliation":[{"name":"College of IoT Engineering Hohai University Changzhou China"}]},{"given":"Jinxiu","family":"Zhu","sequence":"additional","affiliation":[{"name":"College of IoT Engineering Hohai University Changzhou China"},{"name":"Jiangsu Provincial Collaborative Innovation Center of World Water Valley and Water Ecological Civilization Nanjing Jiangsu China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-7020-9905","authenticated-orcid":false,"given":"Xin","family":"Su","sequence":"additional","affiliation":[{"name":"College of IoT Engineering Hohai University Changzhou China"}]},{"given":"Xuewu","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of IoT Engineering Hohai University Changzhou China"}]}],"member":"311","published-online":{"date-parts":[[2019,4,26]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/1360612.1360671"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2016.06.020"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2013.2283302"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2015.2405013"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2008.4587643"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2010.168"},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2016.2598681"},{"key":"e_1_2_7_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46475-6_10"},{"key":"e_1_2_7_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2868567"},{"key":"e_1_2_7_11_1","unstructured":"LarsenABL S\u00f8nderbySK LarochelleH WintherO.Autoencoding beyond pixels using a learned similarity metric. 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