Video Desnowing and Deraining via Saliency and Dual Adaptive Spatiotemporal Filtering

Outdoor 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 o...

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Autores principales:	Yongji Li, Rui Wu, Zhenhong Jia, Jie Yang, Nikola Kasabov
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	video desnowing and deraining saliency adaptive filtering outdoor vision sensing Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/bb63851914f24511a274e56c719da2b8
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id	oai:doaj.org-article:bb63851914f24511a274e56c719da2b8
record_format	dspace
spelling	oai:doaj.org-article:bb63851914f24511a274e56c719da2b82021-11-25T18:57:59ZVideo Desnowing and Deraining via Saliency and Dual Adaptive Spatiotemporal Filtering10.3390/s212276101424-8220https://doaj.org/article/bb63851914f24511a274e56c719da2b82021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7610https://doaj.org/toc/1424-8220Outdoor 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.Yongji LiRui WuZhenhong JiaJie YangNikola KasabovMDPI AGarticlevideo desnowing and derainingsaliencyadaptive filteringoutdoor vision sensingChemical technologyTP1-1185ENSensors, Vol 21, Iss 7610, p 7610 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	video desnowing and deraining saliency adaptive filtering outdoor vision sensing Chemical technology TP1-1185
spellingShingle	video desnowing and deraining saliency adaptive filtering outdoor vision sensing Chemical technology TP1-1185 Yongji Li Rui Wu Zhenhong Jia Jie Yang Nikola Kasabov Video Desnowing and Deraining via Saliency and Dual Adaptive Spatiotemporal Filtering
description	Outdoor 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.
format	article
author	Yongji Li Rui Wu Zhenhong Jia Jie Yang Nikola Kasabov
author_facet	Yongji Li Rui Wu Zhenhong Jia Jie Yang Nikola Kasabov
author_sort	Yongji Li
title	Video Desnowing and Deraining via Saliency and Dual Adaptive Spatiotemporal Filtering
title_short	Video Desnowing and Deraining via Saliency and Dual Adaptive Spatiotemporal Filtering
title_full	Video Desnowing and Deraining via Saliency and Dual Adaptive Spatiotemporal Filtering
title_fullStr	Video Desnowing and Deraining via Saliency and Dual Adaptive Spatiotemporal Filtering
title_full_unstemmed	Video Desnowing and Deraining via Saliency and Dual Adaptive Spatiotemporal Filtering
title_sort	video desnowing and deraining via saliency and dual adaptive spatiotemporal filtering
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/bb63851914f24511a274e56c719da2b8
work_keys_str_mv	AT yongjili videodesnowingandderainingviasaliencyanddualadaptivespatiotemporalfiltering AT ruiwu videodesnowingandderainingviasaliencyanddualadaptivespatiotemporalfiltering AT zhenhongjia videodesnowingandderainingviasaliencyanddualadaptivespatiotemporalfiltering AT jieyang videodesnowingandderainingviasaliencyanddualadaptivespatiotemporalfiltering AT nikolakasabov videodesnowingandderainingviasaliencyanddualadaptivespatiotemporalfiltering
_version_	1718410486379184128

Video Desnowing and Deraining via Saliency and Dual Adaptive Spatiotemporal Filtering

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