{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T08:44:51Z","timestamp":1766133891090,"version":"3.37.3"},"reference-count":26,"publisher":"World Scientific Pub Co Pte Ltd","issue":"09","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundations of China","doi-asserted-by":"crossref","award":["61727802","61501235"],"award-info":[{"award-number":["61727802","61501235"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J CIRCUIT SYST COMP"],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p> In outdoor scenes, haze limits the visibility of images, and degrades people\u2019s judgement of the objects. In this paper, based on an assumption of human visual perception in frequency domain, a novel image haze removal filtering is proposed. Combining this assumption with the theory of frequency domain filtering, we first estimate the cut-off frequency to divide the frequency domain of the hazy image into three components\u00a0\u2014 low-frequency domain, intermediate-frequency domain and high-frequency domain. Then, by introducing the weighting factors, the three components are recombined together. After the theoretical deduction of frequency domain, the establishment of the actual model and adjusting the cut-off frequency and weighting factors, we finally acquire a global and adaptive filtering. This filtering can restore the details and the contours of the images, which have less noise, and improve the visibility of the objects in hazy images. Moreover, our method is simple in structure and strongly applicable, and rarely affected by parameters. Our algorithm is stable and performs well in heavy fog and the scene changes. <\/jats:p>","DOI":"10.1142\/s0218126619501421","type":"journal-article","created":{"date-parts":[[2018,9,3]],"date-time":"2018-09-03T02:43:31Z","timestamp":1535942611000},"page":"1950142","source":"Crossref","is-referenced-by-count":8,"title":["Global Image Dehazing via Frequency Perception Filtering"],"prefix":"10.1142","volume":"28","author":[{"given":"Linli","family":"Xu","sequence":"first","affiliation":[{"name":"Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing University of Science and Technology, Nanjing 210094, P.\u00a0R.\u00a0China"}]},{"given":"Jing","family":"Han","sequence":"additional","affiliation":[{"name":"Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing University of Science and Technology, Nanjing 210094, P.\u00a0R.\u00a0China"}]},{"given":"Tian","family":"Wang","sequence":"additional","affiliation":[{"name":"Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing University of Science and Technology, Nanjing 210094, P.\u00a0R.\u00a0China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6688-4529","authenticated-orcid":false,"given":"Lianfa","family":"Bai","sequence":"additional","affiliation":[{"name":"Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing University of Science and Technology, Nanjing 210094, P.\u00a0R.\u00a0China"}]}],"member":"219","published-online":{"date-parts":[[2019,9,2]]},"reference":[{"key":"S0218126619501421BIB001","doi-asserted-by":"publisher","DOI":"10.1364\/JOSA.61.000001"},{"key":"S0218126619501421BIB002","doi-asserted-by":"publisher","DOI":"10.1109\/83.597272"},{"key":"S0218126619501421BIB003","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-12-336156-1.50061-6"},{"key":"S0218126619501421BIB005","first-page":"995","volume":"22","author":"Yang J.","year":"2010","journal-title":"J. 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