结合分层字典学习和空谱信息的多光谱图像去噪

doi:10.3969/j.issn.1001-2400.2017.04.030

Abstract

Abstract:

A novel denoising method is proposed for the multispectral imagery by combining the hierarchical dictionary learning and the spatial-spectral information. First, the band-subset segmentation is developed by exploiting the highly structural correlations between adjacent bands. Second, the hierarchical dictionary learning model with spatial information is applied to sequentially denoise each band-subset. The noise characteristics of the multispectral images is well depicted by decomposing the noise term into the Gaussian noise term and the sparse noise term, and Gibbs sampling is utilized to solve the model. The effectiveness of the proposed method is compared with that of the state-of-the-art approaches and validated on two multispectral images.

Key words: hierarchical dictionary learning, denoising, spatial-spectral information, multispectral images

LIU Shuai;MA Wenping;YANG Shuyuan;CHEN Puhua. Multispectral imagery denoising using hierarchical dictionary learning with spatial-spectral information[J].Journal of Xidian University, 2017, 44(4): 174-180.

References

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Multispectral imagery denoising using hierarchical dictionary learning with spatial-spectral information

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