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.2021 Nov 16;21(22):7610.
doi: 10.3390/s21227610.

Video Desnowing and Deraining via Saliency and Dual Adaptive Spatiotemporal Filtering

Affiliations

Video Desnowing and Deraining via Saliency and Dual Adaptive Spatiotemporal Filtering

Yongji Li et al. Sensors (Basel)..

Abstract

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.

Keywords: adaptive filtering; outdoor vision sensing; saliency; video desnowing and deraining.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The flow diagram of our proposed algorithm.
Figure 2
Figure 2
Extracting the low-rank background (b) from a snow video sequence (a).
Figure 3
Figure 3
(a) The moving object matching processes, (b) the result of dual adaptive spatiotemporal filtering, (c) the clean video frame obtained by pasting the desnowing moving object back into a low-rank background.
Figure 4
Figure 4
Comparison on a synthetic snow video. (a) Ground truth, (b) input, (c) Kim et al. [16], (d) Wang et al. [8], (e) Li et al. [14], (f) Chen et al. [32], (g) proposed method.
Figure 5
Figure 5
Comparison on a synthetic rain video. (a) Ground truth, (b) input, (c) Kim et al. [16], (d) Wang et al. [8], (e) Li et al. [14], (f) Chen et al. [32], (g) proposed method.
Figure 6
Figure 6
Comparison on a real snow video. (a) Input, (b) Kim et al. [16], (c) Wang et al. [8], (d) Li et al. [14], (e) Chen et al. [32], (f) proposed method.
Figure 7
Figure 7
Comparison on a real rain video. (a) Input, (b) Kim et al. [16], (c) Wang et al. [8], (d) Li et al. [14], (e) Chen et al. [32], (f) proposed method.
Figure 8
Figure 8
Comparison on a real snow video. (a) Input, (b) Kim et al. [16], (c) Wang et al. [8], (d) Li et al. [14], (e) Chen et al. [32], (f) proposed method.
Figure 9
Figure 9
Comparison on a real rain video. (a) Input, (b) Kim et al. [16], (c) Wang et al. [8], (d) Li et al. [14], (e) Chen et al. [32], (f) proposed method.
Figure 10
Figure 10
Comparison on a real snow video. (a) Input, (b) Kim et al. [16], (c) Wang et al. [8], (d) Li et al. [14], (e) Chen et al. [32], (f) proposed method.
Figure 11
Figure 11
Comparison on a real rain video. (a) Input, (b) Kim et al. [16], (c) Wang et al. [8], (d) Li et al. [14], (e) Chen et al. [32], (f) proposed method.
Figure 12
Figure 12
Comparison on a real rain video. (a) Input, (b) Kim et al. [16], (c) Wang et al. [8], (d) Li et al. [14], (e) Chen et al. [32], (f) proposed method.
Figure 13
Figure 13
Runtime comparison of comparable methods on two videos. (a) The test object is the synthetic snow video (Figure 4). (b) The test object is the real rain video (Figure 11).
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