Video Denoising and Object Detection Based on RPCA Model with <i>l</i><sub>1</sub>-TV Regularization Constraints

Fig. 2. Gaussian noise detection results. (a)(b) Static background; (c)(d) dynamic background sequence; (e) turbulent environment sequence; (f) camera shake sequence; (g) high frame rate sequence

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Fig. 3. Gaussian noise detection results with different variances.(a) 1969 frame and (b) 2000 frame in dynamic background; (c) 1689 frame and (d) 1969 frame in static background

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Table 1. Flow of proposed algorithm

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Table 1. Flow of proposed algorithm

Algorithm 1:Video denoising and object detection based on RPCA model with l₁-TV regularization constraints

Input: Original Video V∈R^m^×ⁿ^×^p, then add noise to the video to form a noisy video. Stack the noisy video frames to form O∈R^mn^×^p;Initialization: L⁰, S⁰, E⁰, F⁰, X⁰, Y⁰=0∈R^mn^×^p, k=0, μ_max=1.25/‖Y‖_F, λ₁,λ₂,λ₃>0,μ⁰=10^-2,ρ=1.5;Output: Optimal solution(F^k, K^k)1. While not converged do2. Update L^k⁺¹ via Eq. (12); Update S^k⁺¹ via Eq. (15); Update E^k⁺¹ via Eq. (17);3. Update F^k⁺¹ via Eq. (23); Update X^k⁺¹ via Eq. (24); Update Y^k⁺¹ via Eq. (25);4. Update μ^k⁺¹=min(ρμ^k,μ_max);5. K=K+1;6. Check the convergence condition:

‖ O - L^{k} - F^{k} ‖_{F}^{2}

≤10^-8

‖ O ‖_{F}^{2}

;7. End

Table 2. Measurement values of three indicators of different algorithms

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Table 2. Measurement values of three indicators of different algorithms

Image	DECOLOR			TVRPCA			IBT			LR l₁ TV			Proposed
Image	P	R	F	P	R	F	P	R	F	P	R	F	P	R	F
Fig. 2(a)	0.32	0.54	0.44	0.83	0.57	0.69	0.65	0.51	0.57	0.46	0.70	0.63	0.73	0.89	0.80
Fig. 2(b)	0.84	0.45	0.59	0.79	0.45	0.57	0.50	0.63	0.56	0.72	0.80	0.76	0.88	0.72	0.79
Fig. 2(c)	0.90	0.51	0.65	0.90	0.83	0.86	0.63	0.65	0.64	0.46	0.60	0.51	0.90	0.86	0.88
Fig. 2(d)	0.70	0.69	0.69	0.82	0.51	0.66	0.62	0.31	0.54	0.73	0.87	0.80	0.70	0.93	0.85
Fig. 2(e)	0.22	0.65	0.32	0.78	0.71	0.75	0.72	0.80	0.76	0.73	0.86	0.79	0.83	0.87	0.85
Fig. 2(f)	0.85	0.53	0.68	0.82	0.78	0.80	0.53	0.27	0.36	0.75	0.85	0.80	0.89	0.94	0.91
Fig.2(g)	0.89	0.51	0.64	0.90	0.43	0.58	0.69	0.65	0.67	0.63	0.66	0.64	0.66	0.66	0.66

Table 3. Comparison of running time of five algorithmss

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Table 3. Comparison of running time of five algorithmss

Method	Fig. 2(a)	Fig. 2(b)	Fig. 2(c)	Fig. 2(d)	Fig. 2(e)	Fig. 2(f)	Fig. 2(g)
Proposed	20.04	24.31	29.95	15.30	18.23	13.67	18.19
DECOLOR	30.83	29.12	37.90	25.98	17.98	23.95	37.63
IBT	57.44	48.64	92.56	87.63	76.54	89.63	77.98
TVRPCA	55.23	43.56	63.72	78.03	63.83	74.38	76.93
LRl₁TV	31.32	21.42	30.43	24.78	10.51	20.66	21.58

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Guoliang Yang, Dingling Yu, Zhendong Lai. Video Denoising and Object Detection Based on RPCA Model with l₁-TV Regularization Constraints[J]. Laser & Optoelectronics Progress, 2020, 57(16): 161506

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Paper Information

Category: Machine Vision

Received: Nov. 2, 2019

Accepted: Dec. 11, 2019

Published Online: Aug. 5, 2020

The Author Email: Dingling Yu (ydl_1001@163.com)

DOI:10.3788/LOP57.161506

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology