Video Desnowing and Deraining via Saliency and Dual Adaptive Spatiotemporal Filtering

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aut.relation.endpage7610
aut.relation.issue22en_NZ
aut.relation.journalSensorsen_NZ
aut.relation.startpage7610
aut.relation.volume21en_NZ
aut.researcherKassabov, Nikola
dc.contributor.authorLi, Yen_NZ
dc.contributor.authorWu, Ren_NZ
dc.contributor.authorJia, Zen_NZ
dc.contributor.authorYang, Jen_NZ
dc.contributor.authorKasabov, Nen_NZ
dc.date.accessioned2022-02-08T02:15:32Z
dc.date.available2022-02-08T02:15:32Z
dc.date.copyright2021en_NZ
dc.date.issued2021en_NZ
dc.description.abstractOutdoor vision sensing systems often struggle with poor weather conditions, such as snow and rain, which poses a great challenge to existing video desnowing and deraining methods. In this paper, we propose a novel video desnowing and deraining model that utilizes the salience information of moving objects to address this problem. First, we remove the snow and rain from the video by low-rank tensor decomposition, which makes full use of the spatial location information and the correlation between the three channels of the color video. Second, because existing algorithms often regard sparse snowflakes and rain streaks as moving objects, this paper injects salience information into moving object detection, which reduces the false alarms and missed alarms of moving objects. At the same time, feature point matching is used to mine the redundant information of moving objects in continuous frames, and a dual adaptive minimum filtering algorithm in the spatiotemporal domain is proposed by us to remove snow and rain in front of moving objects. Both qualitative and quantitative experimental results show that the proposed algorithm is more competitive than other state-of-the-art snow and rain removal methods.en_NZ
dc.identifier.citationSensors, 21(22), 7610. https://doi.org/10.3390/s21227610
dc.identifier.doi10.3390/s21227610en_NZ
dc.identifier.issn1424-8220en_NZ
dc.identifier.urihttps://hdl.handle.net/10292/14886
dc.languageenen_NZ
dc.publisherMDPI AGen_NZ
dc.relation.urihttps://www.mdpi.com/1424-8220/21/22/7610
dc.rights© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
dc.rights.accessrightsOpenAccessen_NZ
dc.subjectVideo desnowing and deraining; Saliency; Adaptive filtering; Outdoor vision sensing
dc.titleVideo Desnowing and Deraining via Saliency and Dual Adaptive Spatiotemporal Filteringen_NZ
dc.typeJournal Article
pubs.elements-id444735
pubs.organisational-data/AUT
pubs.organisational-data/AUT/Faculty of Design & Creative Technologies
pubs.organisational-data/AUT/PBRF
pubs.organisational-data/AUT/PBRF/PBRF Design and Creative Technologies
pubs.organisational-data/AUT/PBRF/PBRF Design and Creative Technologies/PBRF ECMS
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