高光谱图像(HSI)具有良好的光谱识别能力,但在采集过程中易受到混合噪声的污染,严重影响了后续任务的精度,因此HSI去噪是重要的预处理过程。针对现有去噪方法对空间-光谱先验信息利用不足、条纹噪声建模不合理的问题,提出一种新的基于...高光谱图像(HSI)具有良好的光谱识别能力,但在采集过程中易受到混合噪声的污染,严重影响了后续任务的精度,因此HSI去噪是重要的预处理过程。针对现有去噪方法对空间-光谱先验信息利用不足、条纹噪声建模不合理的问题,提出一种新的基于群稀疏正则化的高光谱图像去噪算法。该算法将干净HSI的空间-光谱低秩特性和各波段上条纹噪声的低秩结构融入一个新框架,实现了干净HSI与高强度结构化条纹噪声的分离;同时为了有效保持图像的边缘信息,在去噪模型中引入新的群稀疏正则化,即基于L_(2,1)范数的增强型三维全变分正则化(enhanced 3D total variation, E3DTV),充分挖掘HSI差分图像的稀疏先验信息,进一步提升了图像的分段平滑性。采用交替方向乘子法对变量优化求解,在仿真和真实数据集上进行数值实验表明,所提模型具有更好的去噪和去条纹性能,在视觉效果和定量评价结果上都明显优于其他对比算法。展开更多
As a kind of weaker supervisory information, pairwise constraints can be exploited to guide the data analysis process, such as data clustering. This paper formulates pairwise constraint propagation, which aims to pred...As a kind of weaker supervisory information, pairwise constraints can be exploited to guide the data analysis process, such as data clustering. This paper formulates pairwise constraint propagation, which aims to predict the large quantity of unknown constraints from scarce known constraints, as a low-rank matrix recovery(LMR) problem. Although recent advances in transductive learning based on matrix completion can be directly adopted to solve this problem, our work intends to develop a more general low-rank matrix recovery solution for pairwise constraint propagation, which not only completes the unknown entries in the constraint matrix but also removes the noise from the data matrix. The problem can be effectively solved using an augmented Lagrange multiplier method. Experimental results on constrained clustering tasks based on the propagated pairwise constraints have shown that our method can obtain more stable results than state-of-the-art algorithms,and outperform them.展开更多
为了准确检测铝箔表面的穿孔、污点、亮斑和刮痕等各种缺陷,提出了一种基于低秩稀疏分解的铝箔图像表面缺陷检测方法。铝箔材料生产过程中表面出现缺陷的概率较小,同时一幅铝箔图像中缺陷占整幅图像的比例较小,即铝箔图像背景之间是线...为了准确检测铝箔表面的穿孔、污点、亮斑和刮痕等各种缺陷,提出了一种基于低秩稀疏分解的铝箔图像表面缺陷检测方法。铝箔材料生产过程中表面出现缺陷的概率较小,同时一幅铝箔图像中缺陷占整幅图像的比例较小,即铝箔图像背景之间是线性相关的,可近似视为处于同一低秩子空间中,同时图像表面缺陷是近似稀疏的。采用RPCA(Robust Principal Component Analysis)算法对铝箔图像序列组成的观测数据矩阵进行低秩稀疏分解,得到低秩的背景图像和稀疏的缺陷图像。分别对单幅铝箔图像以及由多幅铝箔图像组成的图像序列进行低秩稀疏分解实验,在铝箔图像表面缺陷检测应用中验证所提方法的有效性。实验结果表明,提出方法检测到的缺陷清晰、完整,处理一幅大小为880×540的铝箔图像平均耗时不超过0.7秒,能够实现铝箔表面缺陷的实时检测。同时,算法具有较好的扩展性,能够方便地应用到其他产品的表面缺陷检测中。展开更多
文摘高光谱图像(HSI)具有良好的光谱识别能力,但在采集过程中易受到混合噪声的污染,严重影响了后续任务的精度,因此HSI去噪是重要的预处理过程。针对现有去噪方法对空间-光谱先验信息利用不足、条纹噪声建模不合理的问题,提出一种新的基于群稀疏正则化的高光谱图像去噪算法。该算法将干净HSI的空间-光谱低秩特性和各波段上条纹噪声的低秩结构融入一个新框架,实现了干净HSI与高强度结构化条纹噪声的分离;同时为了有效保持图像的边缘信息,在去噪模型中引入新的群稀疏正则化,即基于L_(2,1)范数的增强型三维全变分正则化(enhanced 3D total variation, E3DTV),充分挖掘HSI差分图像的稀疏先验信息,进一步提升了图像的分段平滑性。采用交替方向乘子法对变量优化求解,在仿真和真实数据集上进行数值实验表明,所提模型具有更好的去噪和去条纹性能,在视觉效果和定量评价结果上都明显优于其他对比算法。
基金supported by the National Natural Science Foundation of China (No. 61300164)
文摘As a kind of weaker supervisory information, pairwise constraints can be exploited to guide the data analysis process, such as data clustering. This paper formulates pairwise constraint propagation, which aims to predict the large quantity of unknown constraints from scarce known constraints, as a low-rank matrix recovery(LMR) problem. Although recent advances in transductive learning based on matrix completion can be directly adopted to solve this problem, our work intends to develop a more general low-rank matrix recovery solution for pairwise constraint propagation, which not only completes the unknown entries in the constraint matrix but also removes the noise from the data matrix. The problem can be effectively solved using an augmented Lagrange multiplier method. Experimental results on constrained clustering tasks based on the propagated pairwise constraints have shown that our method can obtain more stable results than state-of-the-art algorithms,and outperform them.
文摘为了准确检测铝箔表面的穿孔、污点、亮斑和刮痕等各种缺陷,提出了一种基于低秩稀疏分解的铝箔图像表面缺陷检测方法。铝箔材料生产过程中表面出现缺陷的概率较小,同时一幅铝箔图像中缺陷占整幅图像的比例较小,即铝箔图像背景之间是线性相关的,可近似视为处于同一低秩子空间中,同时图像表面缺陷是近似稀疏的。采用RPCA(Robust Principal Component Analysis)算法对铝箔图像序列组成的观测数据矩阵进行低秩稀疏分解,得到低秩的背景图像和稀疏的缺陷图像。分别对单幅铝箔图像以及由多幅铝箔图像组成的图像序列进行低秩稀疏分解实验,在铝箔图像表面缺陷检测应用中验证所提方法的有效性。实验结果表明,提出方法检测到的缺陷清晰、完整,处理一幅大小为880×540的铝箔图像平均耗时不超过0.7秒,能够实现铝箔表面缺陷的实时检测。同时,算法具有较好的扩展性,能够方便地应用到其他产品的表面缺陷检测中。
