{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:21:51Z","timestamp":1760239311581,"version":"build-2065373602"},"reference-count":40,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2020,11,5]],"date-time":"2020-11-05T00:00:00Z","timestamp":1604534400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Institute for Information &amp; communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) Intelligent Defense Boundary Surveillance Technology Using Collaborative Reinforced Learning of Embedded Edge Camera and Image Analysis","award":["2017-0-00250"],"award-info":[{"award-number":["2017-0-00250"]}]},{"name":"ICT R&amp;D program of MSIP\/IITP development of global multi-target tracking and event prediction techniques based on real-time large-scale video analysis","award":["2014-0-00077"],"award-info":[{"award-number":["2014-0-00077"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In a hazy environment, visibility is reduced and objects are difficult to identify. For this reason, many dehazing techniques have been proposed to remove the haze. Especially, in the case of the atmospheric scattering model estimation-based method, there is a problem of distortion when inaccurate models are estimated. We present a novel residual-based dehazing network model to overcome the performance limitation in an atmospheric scattering model-based method. More specifically, the proposed model adopted the gate fusion network that generates the dehazed results using a residual operator. To further reduce the divergence between the clean and dehazed images, the proposed discriminator distinguishes dehazed results and clean images, and then reduces the statistical difference via adversarial learning. To verify each element of the proposed model, we hierarchically performed the haze removal process in an ablation study. Experimental results show that the proposed method outperformed state-of-the-art approaches in terms of peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), international commission on illumination cie delta e 2000 (CIEDE2000), and mean squared error (MSE). It also gives subjectively high-quality images without color distortion or undesired artifacts for both synthetic and real-world hazy images.<\/jats:p>","DOI":"10.3390\/s20216311","type":"journal-article","created":{"date-parts":[[2020,11,5]],"date-time":"2020-11-05T09:04:34Z","timestamp":1604567074000},"page":"6311","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Gated Dehazing Network via Least Square Adversarial Learning"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3303-4341","authenticated-orcid":false,"given":"Eunjae","family":"Ha","sequence":"first","affiliation":[{"name":"Department of Image, Chung-Ang University, Seoul 06974, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3818-6587","authenticated-orcid":false,"given":"Joongchol","family":"Shin","sequence":"additional","affiliation":[{"name":"Department of Image, Chung-Ang University, Seoul 06974, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8593-7155","authenticated-orcid":false,"given":"Joonki","family":"Paik","sequence":"additional","affiliation":[{"name":"Department of Image, Chung-Ang University, Seoul 06974, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1814","DOI":"10.1109\/TCSVT.2014.2317854","article-title":"Visibility Restoration of Single Hazy Images Captured in Real-World Weather Conditions","volume":"24","author":"Huang","year":"2014","journal-title":"IEEE Trans. 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