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color distortion, low contrast, and motion blur in underwater robot photography. The proposed method is built upon a preprocessing framework using a generative adversarial network. ECO-GAN incorporates a convolutional neural network that specifically targets three underwater issues: motion blur, low brightness, and color deviation. To optimize computation and inference speed, an encoder is employed to extract features, whereas different enhancement tasks are handled by dedicated decoders. Moreover, ECO-GAN employs cross-stage fusion modules between the decoders to strengthen the connection and enhance the quality of output images. The model is trained using supervised learning with paired datasets, enabling blind image enhancement without additional physical knowledge or prior information. Experimental results demonstrate that ECO-GAN effectively achieves denoising, deblurring, and color deviation removal simultaneously. Compared with methods relying on individual modules or simple combinations of multiple modules, our proposed method achieves superior underwater image enhancement and offers the flexibility for expansion into multiple underwater image enhancement functions.<\/jats:p>","DOI":"10.3390\/s23135774","type":"journal-article","created":{"date-parts":[[2023,6,21]],"date-time":"2023-06-21T02:30:51Z","timestamp":1687314651000},"page":"5774","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["An Underwater Image Enhancement Method for a Preprocessing Framework Based on Generative Adversarial Network"],"prefix":"10.3390","volume":"23","author":[{"given":"Xiao","family":"Jiang","sequence":"first","affiliation":[{"name":"College of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0900-5276","authenticated-orcid":false,"given":"Haibin","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, China"},{"name":"Ningbo Institute of Oceanography, Ningbo 315832, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaxin","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mian","family":"Pan","sequence":"additional","affiliation":[{"name":"College of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6373-5846","authenticated-orcid":false,"given":"Zhu","family":"Li","sequence":"additional","affiliation":[{"name":"College of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingbiao","family":"Liu","sequence":"additional","affiliation":[{"name":"Ocean Technology and Equipment Research Center, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9822-9959","authenticated-orcid":false,"given":"Shuaishuai","family":"Lv","sequence":"additional","affiliation":[{"name":"College of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1109\/MCG.2016.26","article-title":"Underwater Depth Estimation and Image Restoration Based on Single Images","volume":"36","author":"Drews","year":"2016","journal-title":"IEEE Comput. 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