{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T03:24:54Z","timestamp":1762917894079,"version":"build-2065373602"},"reference-count":25,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2018,8,9]],"date-time":"2018-08-09T00:00:00Z","timestamp":1533772800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61701524;61372167;61379104"],"award-info":[{"award-number":["61701524;61372167;61379104"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>To solve the problems of color distortion and structure blurring in images acquired by sensors during bad weather, an image dehazing algorithm based on feature learning is put forward to improve the quality of sensor images. First, we extracted the multiscale structure features of the haze images by sparse coding and the various haze-related color features simultaneously. Then, the generative adversarial network (GAN) was used for sample training to explore the mapping relationship between different features and the scene transmission. Finally, the final haze-free image was obtained according to the degradation model. Experimental results show that the method has obvious advantages in its detail recovery and color retention. In addition, it effectively improves the quality of sensor images.<\/jats:p>","DOI":"10.3390\/s18082606","type":"journal-article","created":{"date-parts":[[2018,8,9]],"date-time":"2018-08-09T10:36:31Z","timestamp":1533810991000},"page":"2606","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A Sensor Image Dehazing Algorithm Based on Feature Learning"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5667-785X","authenticated-orcid":false,"given":"Kun","family":"Liu","sequence":"first","affiliation":[{"name":"College of Aeronautics Engineering, Air Force Engineering University, Xi\u2019an 710038, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linyuan","family":"He","sequence":"additional","affiliation":[{"name":"College of Aeronautics Engineering, Air Force Engineering University, Xi\u2019an 710038, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shiping","family":"Ma","sequence":"additional","affiliation":[{"name":"College of Aeronautics Engineering, Air Force Engineering University, Xi\u2019an 710038, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shan","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Aeronautics Engineering, Air Force Engineering University, Xi\u2019an 710038, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Duyan","family":"Bi","sequence":"additional","affiliation":[{"name":"College of Aeronautics Engineering, Air Force Engineering University, Xi\u2019an 710038, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,8,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1016\/S0030-3992(03)00078-1","article-title":"CMOS vs. CCD sensors in speckle interferometry","volume":"35","author":"Helmers","year":"2003","journal-title":"Opt. 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