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Remote sensing images destriping with an enhanced low-rank prior and total variation regulation

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Abstract

Recovering essential components from noise-corrupted images is a fundamental task for remote sensing-based systems. Discovering or designing valuable prior knowledge is of great importance for the recovery task. In this paper, we present an enhanced low-rank prior to estimate the stripe noise in hyperspectral images (HSIs). By analyzing the structural properties of stripe noise, we extend the low-rank prior from the spatial domain to the gradient domain and propose an enhanced prior that combines the dual low-rank properties. By integrating this prior with variation model, an enhanced low-rank total variation model (ELRTV) is formulated. Extensive experiments on both simulated data and real data demonstrate that the proposed destriping model can effectively remove the stripe noise regularly and preserve more fine-scale details, while introducing no additional artifacts.

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Notes

  1. https://engineering.purdue.edu/~biehl/MultiSpec/hyperspectral.html.

  2. https://modis.gsfc.nasa.gov/data/.

  3. https://aviris.jpl.nasa.gov/dataportal/.

  4. https://ladsweb.modaps.eosdis.nasa.gov/search/.

  5. https://sites.google.com/site/feiyunzhuhomepage/datasets-ground-truths.

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Funding

This work was funded by the Northeast Electric Power University Doctoral Scientific Research Foundation No. 11891.

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Authors and Affiliations

  1. School of Computer Science, Northeast Electric Power University, Jilin, 132012, People’s Republic of China

    Qiong Song & Hongyin Ni

  2. Artificial Intelligence School, Wuchang University of Technology, Wuhan, 430223, People’s Republic of China

    Zhenghua Huang

  3. Xi’an Modern Control Technology Research Institute, Xi’an, 710065, People’s Republic of China

    Kun Bai

  4. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, 300382, People’s Republic of China

    Zhengtao Li

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  1. Qiong Song
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  2. Zhenghua Huang
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  3. Hongyin Ni
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Correspondence to Zhengtao Li.

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Song, Q., Huang, Z., Ni, H. et al. Remote sensing images destriping with an enhanced low-rank prior and total variation regulation. SIViP 16, 1895–1903 (2022). https://doi.org/10.1007/s11760-022-02149-8

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  • DOI: https://doi.org/10.1007/s11760-022-02149-8

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