{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T09:03:53Z","timestamp":1779959033228,"version":"3.53.1"},"reference-count":34,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T00:00:00Z","timestamp":1779926400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Research Grants Council of the Hong Kong Special Administrative Region, China","award":["UGC\/FDS13\/E01\/23"],"award-info":[{"award-number":["UGC\/FDS13\/E01\/23"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Accurate meteorological visibility estimation is vital for transportation safety and environmental monitoring. However, modeling the inherent nonlinear spatial and spectral degradations in hazy environments remains challenging. While recent Large Vision-Language Models (LVLMs) offer strong scene understanding, they lack the regression precision required for visibility estimation. In this paper, we propose the Visibility-Aware Refined CNN (VISR-CNN), a dual-stream architecture that synthesizes local spatial cues with global frequency-domain signatures. The model integrates a Multi-Scale Transmission Attention (MSTA) module, which uses parallel dilated convolutions to estimate atmospheric transmission, and a Global Frequency Branch that utilizes 2D Real Fast Fourier Transforms (RFFT) with Spectral Gating to quantify visibility-dependent blurring. A progressive training strategy is introduced to decouple spectral and spatial optimization, and a physics-informed loss function is designed to supervise numerical regression while enforcing a monotonic ranking constraint consistent with physical light-attenuation laws. Results on the HKCHC-VD dataset show that VISR-CNN achieves state-of-the-art performance (MAE: 1.54 km; RMSE: 2.31 km), representing a 13.0% improvement over VisNet. Further evaluations on the CP1 and SWH datasets confirm robust generalization, reducing overall MAE by 21% and 20%, respectively, compared with the hybrid ResNeXt-50 + ViT model. Notably, in safety-critical range (0\u201310 km), VISR-CNN reduces RMSE for the HKCHC-VD, CP1, and SWH datasets by approximately 55%, 64%, and 71%, respectively, when compared with VisNet. These findings demonstrate the superiority of specialized, physics-grounded architectures over general-purpose LVLMs for high-precision meteorological regression.<\/jats:p>","DOI":"10.3390\/a19060434","type":"journal-article","created":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T08:00:36Z","timestamp":1779955236000},"page":"434","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["VISR-CNN: A Dual-Stream Framework for Meteorological Visibility Estimation via Multi-Scale Transmission Attention and Spectral Gating"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8954-7023","authenticated-orcid":false,"given":"Wai Lun","family":"Lo","sequence":"first","affiliation":[{"name":"Department of Computer Science, Hong Kong Chu Hai College, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kwok Wai","family":"Wong","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Hong Kong Chu Hai College, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7699-1937","authenticated-orcid":false,"given":"Richard Tai Chiu","family":"Hsung","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Hong Kong Chu Hai College, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4890-8256","authenticated-orcid":false,"given":"Henry Shu Hung","family":"Chung","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, City University of Hong Kong, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2246-7552","authenticated-orcid":false,"given":"Hong","family":"Fu","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Information Technology, Education University of Hong Kong, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8578-0696","authenticated-orcid":false,"given":"Harris Sik Ho","family":"Tsang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Hong Kong Chu Hai College, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1231-1386","authenticated-orcid":false,"given":"Tony Yulin","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Hong Kong Chu Hai College, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2341","DOI":"10.1109\/TPAMI.2010.168","article-title":"Single image haze removal using dark channel prior","volume":"33","author":"He","year":"2011","journal-title":"IEEE Trans. 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