Fast Video Rain Removal Algorithm Based on Layer Priors

Fig. 1. Gradient histogram of the background layer. (a) Image in the rainless video; (b) gradient in the vertical direction; (c) gradient in the time direction

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Fig. 2. Gradient histogram of the rain trace layer. (a) Image in the rain trace video; (b) gray histogram; (c) gradient in the horizontal direction

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Fig. 3. Recognition results of different algorithms on the dataset man. (a) Input image; (b) original image; (c) algorithm of Ref. [2]; (d) algorithm of Ref. [3]; (e) algorithm of Ref. [6]; (f) our algorithm

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Fig. 4. Recognition results of different algorithms on the dataset trunk. (a) Input image; (b) original image; (c) algorithm of Ref. [2]; (d) algorithm of Ref. [3]; (e) algorithm of Ref. [6]; (f) our algorithm

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Fig. 5. Processing results of different algorithms on real images with rain. (a) Input image; (b) algorithm of Ref. [2]; (c) algorithm of Ref. [3]; (d) algorithm of Ref. [6]; (e) our algorithm

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Table 1. Flow of our algorithm

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Table 1. Flow of our algorithm

Input: rain image, pre-trained GMM model
Initialization: B=O,R=0, train a lightweight GMM model online
Step1: update M,N by soft threshold algorithm
Step2: update B,R by FTV algorithm
Step3: update $g_{B_{i}}$ , $g_{R_{i}}$ by approximate optimization algorithm
End: meet the convergence condition Eq. (14) or reach the maximum number of iterations, otherwise go back to step1 and iterate again
Output: background layer B and rain trace layer R

Table 2. Experimental results

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Table 2. Experimental results

Dataset	Algorithm	PSNR /dB	SSIM	Time /s
Man	Ref. [2]	37.02	0.981	137.4
	Ref. [3]	33.60	0.989	1332.7
	Ref. [6]	36.18	0.885	515.8
	ours	39.79	0.993	52.9
Dataset	Algorithm	PSNR /dB	SSIM	Time /s
Trunk	Ref. [2]	32.38	0.963	140.6
	Ref. [3]	32.86	0.962	1811.2
	Ref. [6]	30.32	0.938	462.2
	ours	34.57	0.976	51.9
Park	Ref. [2]	34.07	0.991	101.4
	Ref. [3]	33.17	0.970	1501.3
	Ref. [6]	24.18	0.796	393.1
	ours	35.92	0.983	44.0
Highway	Ref. [2]	24.20	0.953	140.1
	Ref. [3]	24.12	0.936	1850.2
	Ref. [6]	23.78	0.868	498.7
	ours	24.81	0.958	50.2