Fidelity-Beltrami-Sparsity Model for Inverse Problems in Multichannel Image Processing

Fidelity-Beltrami-Sparsity Model for Inverse Problems in Multichannel Image Processing
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多通道图像处理中逆问题的 Fidelity-Beltrami-稀疏模型

DOI:
10.1137/120862168
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发表时间:
2013
期刊:
SIAM Journal of Imaging Sciences
影响因子:
--
通讯作者:
Fengxia Yan
Fengxia Yan
中科院分区:
其他
文献类型:
--
作者:
Zelong Wang;Jubo Zhu;Fengxia Yan

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在多通道图像处理中,图像去噪、图像去模糊、图像修复等许多任务都可以归结为反问题和高维数据处理领域。要获得更好的性能,有两个关键点需要认真考虑。一方面,这些反问题大多是不适定的,而先验约束是通过缩减解空间将它们转化为适定问题所必需的。另一方面,在多通道图像处理中,不同通道之间的耦合信息是重要的,其中图像的每个通道通常是单独处理的。本文提出了多通道图像处理反问题的保真度-贝特拉米-稀疏性(FBS)模型,该模型可以同时利用两个重要的先验,即光滑性先验和稀疏先验。众所周知,光滑性先验和稀疏性先验分别在图像中得到了广泛的验证,因此反问题需要解决。
In multichannel image processing, many tasks, such as image denoising, image deblurring, and image inpainting, can be included in the fields of inverse problems and high-dimensional data processing. To get better performance, there are two key points that should be considered seriously. On the one hand, most of these inverse problems are ill-posed, and the prior constraints are necessary to transform them into well-posed problems by reducing the solution space. On the other hand, the information of the coupling among different channels is important in multichannel image processing, where each channel of the image is usually processed separately. In this paper, we propose the fidelity-Beltrami-sparsity (FBS) model for the inverse problems in multichannel image processing, which can simultaneously exploit two important priors, i.e., the smoothness prior and the sparsity prior. As we know, the smoothness prior and the sparsity prior have been widely validated for an image, respectively, so inverse problems w...
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