Next Article in Journal
Semi-Automatic Spectral Image Stitching for a Compact Hybrid Linescan Hyperspectral Camera towards Near Field Remote Monitoring of Potato Crop Leaves
Previous Article in Journal
Sensing Advancement and Health Monitoring of Transport Structures
 
 
Article

Video Desnowing and Deraining via Saliency and Dual Adaptive Spatiotemporal Filtering

1
College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
2
Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200400, China
3
Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland 1020, New Zealand
*
Author to whom correspondence should be addressed.
Academic Editor: Christophoros Nikou
Sensors 2021, 21(22), 7610; https://doi.org/10.3390/s21227610
Received: 30 October 2021 / Revised: 12 November 2021 / Accepted: 15 November 2021 / Published: 16 November 2021
(This article belongs to the Section Sensing and Imaging)
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. View Full-Text
Keywords: video desnowing and deraining; saliency; adaptive filtering; outdoor vision sensing video desnowing and deraining; saliency; adaptive filtering; outdoor vision sensing
Show Figures

Figure 1

MDPI and ACS Style

Li, Y.; Wu, R.; Jia, Z.; Yang, J.; Kasabov, N. Video Desnowing and Deraining via Saliency and Dual Adaptive Spatiotemporal Filtering. Sensors 2021, 21, 7610. https://doi.org/10.3390/s21227610

AMA Style

Li Y, Wu R, Jia Z, Yang J, Kasabov N. Video Desnowing and Deraining via Saliency and Dual Adaptive Spatiotemporal Filtering. Sensors. 2021; 21(22):7610. https://doi.org/10.3390/s21227610

Chicago/Turabian Style

Li, Yongji, Rui Wu, Zhenhong Jia, Jie Yang, and Nikola Kasabov. 2021. "Video Desnowing and Deraining via Saliency and Dual Adaptive Spatiotemporal Filtering" Sensors 21, no. 22: 7610. https://doi.org/10.3390/s21227610

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop