针对目前红外与可见光图像融合算法中由于源图像信息特征不同而产生的全局结构和细节信息无法保留等问题,提出一种基于低秩稀疏分解(Low-rank Sparse Decomposition,LRSD)的红外与可见光图像融合方法。该方法通过最优方向选择(Method of...针对目前红外与可见光图像融合算法中由于源图像信息特征不同而产生的全局结构和细节信息无法保留等问题,提出一种基于低秩稀疏分解(Low-rank Sparse Decomposition,LRSD)的红外与可见光图像融合方法。该方法通过最优方向选择(Method of Optimal Directions,MOD)、K奇异值分解(K-Singular Value Decomposition,K-SVD)和背景字典3种字典学习方法构造字典,并采用低秩表示(Low-rank Represention,LRR)对源图像分解得到低秩部分和稀疏细节部分,其中低秩部分保留了源图像的全局结构,稀疏部分突出了源图像的局部结构和细节信息。在融合过程中,对低秩部分和稀疏部分分别采用加权平均与l_(2)-l_(1)范数约束策略进行融合,保留了全局对比度和像素强度。实验结果表明,与经典融合算法相比,提出的方法在图像视觉效果和定量评价指标方面有显著提升。采用MOD和K-SVD方法进行字典训练,得到的融合图像在定量评价指标互信息(Mutual Information,MI)、结构相似度(Structural Similarity Index,SSIM)、视觉信息保真度(Visual Information Fidelity,VIF)、标准差(Standard Deviation,SD)和峰值信噪比(Peak Signal to Noise Ratio,PSNR)上分别提高了约6%、27%、9.6%、2.4%和3.4%;采用背景字典方法进行字典训练,得到的融合图像在定量评价指标MI、SSIM、SD、均方误差(Mean Squared Error,MSE)、PSNR上分别提高了约23%、29%、1.2%、33%和4.5%。展开更多
针对传统低秩稀疏分解(low rank and sparse decomposition,LRSD)用于视频运动目标检测时检测精度较低的问题,提出了一种鲁棒非凸运动辅助LRSD(robust nonconvex motion-assisted LRSD,RNMALRSD)的运动目标检测算法。该算法首先考虑到...针对传统低秩稀疏分解(low rank and sparse decomposition,LRSD)用于视频运动目标检测时检测精度较低的问题,提出了一种鲁棒非凸运动辅助LRSD(robust nonconvex motion-assisted LRSD,RNMALRSD)的运动目标检测算法。该算法首先考虑到视频背景的低秩特性,采用非凸γ范数对秩函数进行逼近,考虑视频背景在变换域上仍然具有稀疏性,引入背景在变换域的稀疏先验。其次,引入运动辅助信息矩阵,使其融入前景的运动信息,表示每个像素属于背景的可能性,提高视频运动目标检测的准确度。然后,采用交替方向乘子法(alternating direction method of multipliers,ADMM)对提出的模型进行求解。最后,将提出的方法应用到视频运动目标检测上进行仿真实验。对实验结果的分析表明,提出的RNMALRSD方法比其他基于LRSD的运动目标检测方法具有更高的检测精度。展开更多
Background subtraction is a challenging problem in surveillance scenes. Although the low-rank and sparse decomposition(LRSD) methods offer an appropriate framework for background modeling, they fail to account for ima...Background subtraction is a challenging problem in surveillance scenes. Although the low-rank and sparse decomposition(LRSD) methods offer an appropriate framework for background modeling, they fail to account for image's local structure, which is favorable for this problem. Based on this, we propose a background subtraction method via low-rank and SILTP-based structured sparse decomposition, named LRSSD. In this method, a novel SILTP-inducing sparsity norm is introduced to enhance the structured presentation of the foreground region. As an assistance, saliency detection is employed to render a rough shape and location of foreground. The final refined foreground is decided jointly by sparse component and attention map. Experimental results on different datasets show its superiority over the competing methods, especially under noise and changing illumination scenarios.展开更多
文摘针对目前红外与可见光图像融合算法中由于源图像信息特征不同而产生的全局结构和细节信息无法保留等问题,提出一种基于低秩稀疏分解(Low-rank Sparse Decomposition,LRSD)的红外与可见光图像融合方法。该方法通过最优方向选择(Method of Optimal Directions,MOD)、K奇异值分解(K-Singular Value Decomposition,K-SVD)和背景字典3种字典学习方法构造字典,并采用低秩表示(Low-rank Represention,LRR)对源图像分解得到低秩部分和稀疏细节部分,其中低秩部分保留了源图像的全局结构,稀疏部分突出了源图像的局部结构和细节信息。在融合过程中,对低秩部分和稀疏部分分别采用加权平均与l_(2)-l_(1)范数约束策略进行融合,保留了全局对比度和像素强度。实验结果表明,与经典融合算法相比,提出的方法在图像视觉效果和定量评价指标方面有显著提升。采用MOD和K-SVD方法进行字典训练,得到的融合图像在定量评价指标互信息(Mutual Information,MI)、结构相似度(Structural Similarity Index,SSIM)、视觉信息保真度(Visual Information Fidelity,VIF)、标准差(Standard Deviation,SD)和峰值信噪比(Peak Signal to Noise Ratio,PSNR)上分别提高了约6%、27%、9.6%、2.4%和3.4%;采用背景字典方法进行字典训练,得到的融合图像在定量评价指标MI、SSIM、SD、均方误差(Mean Squared Error,MSE)、PSNR上分别提高了约23%、29%、1.2%、33%和4.5%。
文摘针对传统低秩稀疏分解(low rank and sparse decomposition,LRSD)用于视频运动目标检测时检测精度较低的问题,提出了一种鲁棒非凸运动辅助LRSD(robust nonconvex motion-assisted LRSD,RNMALRSD)的运动目标检测算法。该算法首先考虑到视频背景的低秩特性,采用非凸γ范数对秩函数进行逼近,考虑视频背景在变换域上仍然具有稀疏性,引入背景在变换域的稀疏先验。其次,引入运动辅助信息矩阵,使其融入前景的运动信息,表示每个像素属于背景的可能性,提高视频运动目标检测的准确度。然后,采用交替方向乘子法(alternating direction method of multipliers,ADMM)对提出的模型进行求解。最后,将提出的方法应用到视频运动目标检测上进行仿真实验。对实验结果的分析表明,提出的RNMALRSD方法比其他基于LRSD的运动目标检测方法具有更高的检测精度。
基金supported in part by the EU FP7 QUICK project under Grant Agreement No.PIRSES-GA-2013-612652*National Nature Science Foundation of China(No.61671336,61502348,61231015,61671332,U1736206)+3 种基金Hubei Province Technological Innovation Major Project(No.2016AAA015,No.2017AAA123)the Fundamental Research Funds for the Central Universities(413000048)National High Technology Research and Development Program of China(863 Program)No.2015AA016306Applied Basic Research Program of Wuhan City(2016010101010025)
文摘Background subtraction is a challenging problem in surveillance scenes. Although the low-rank and sparse decomposition(LRSD) methods offer an appropriate framework for background modeling, they fail to account for image's local structure, which is favorable for this problem. Based on this, we propose a background subtraction method via low-rank and SILTP-based structured sparse decomposition, named LRSSD. In this method, a novel SILTP-inducing sparsity norm is introduced to enhance the structured presentation of the foreground region. As an assistance, saliency detection is employed to render a rough shape and location of foreground. The final refined foreground is decided jointly by sparse component and attention map. Experimental results on different datasets show its superiority over the competing methods, especially under noise and changing illumination scenarios.
文摘针对探地雷达应用于地雷探测时的强杂波干扰问题,提出一种基于低秩稀疏分解的杂波抑制方法。该方法将加权核范数(weighted nuclear norm,WNN)引入稳健主成分分析(robust principle component analysis,RPCA)方法,结合随机奇异值分解(randomized singular value decomposition,RSVD)与交替方向乘子(alternating direction method of multipliers,ADMM)法来求解表征杂波的低秩矩阵及表征目标的稀疏成分,提高了算法的精度与效率。从实验结果来看,所提方法能够有效改善成像结果的信杂比,且运算效率优于RPCA方法5倍以上,表明该方法能精确划分目标与杂波,有效实现杂波抑制。
