TVRPCA＋: Low-rank and sparse decomposition based on spectral norm and structural sparsity-inducing norm
TVRPCA＋: Low-rank and sparse decomposition based on spectral norm and structural sparsity-inducing norm
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DOI：
10.1016/j.sigpro.2023.109319
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发表时间：
2023-11
期刊：
影响因子：
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通讯作者：
Ruibo Fan;Mingli Jing;Jingang Shi;Lan Li;Zizhao Wang
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文献类型：
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作者：
Ruibo Fan;Mingli Jing;Jingang Shi;Lan Li;Zizhao Wang
在前景背景分离中，由于复杂的视频环境和噪声的影响，传统的低阶稀疏分解算法难以获得清晰、完整的前景表示。针对这一问题，我们提出了一种基于谱范数、结构稀疏范数和全变差正则化的更健壮、更高性能的低阶稀疏分解算法TVRPCA+。利用结构化稀疏范数和TV正则化来抑制噪声，获得更清晰的前景。该算法对低阶分量使用谱范数，解决了过度惩罚的问题，恢复了更多的前景信息。此外，还设计了一种基于不精确增广拉格朗日乘子法的高效算法来求解该优化问题。实验结果表明，在8个复杂背景的无噪声测试视频序列中，TVRPCA+获得了5个最高的F度量和3个次高的F度量，同时在所有10个有噪声的试验组中也获得了最高的平均F度量。
Traditional low-rank sparse decomposition algorithms have trouble obtaining a clear and complete foreground representation in foreground–background separation due to the complex video environment and the noise. For this issue, We propose a more robust and higher-performance low-rank and sparse decomposition algorithm named TVRPCA+ based on spectral norm, structured sparse norm and total variation (TV) regularization. The structured sparse norm and TV regularization are exploited to suppress noise and obtain much cleaner foregrounds. Spectral norm is used in our algorithm for the low-rank component to address the issue of over-punishment and restore more foreground information. Moreover, an efficient algorithm based on the inexact augmented Lagrange multiplier method is designed to solve the proposed optimization problem. Experimental results show that TVRPCA+ obtained five top F-measures and three of the second-highest F-measures in eight noise-free test video sequences with complex backgrounds, while the highest average F-measure was also achieved in all ten experimental groups with noise.