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arXiv · 2407.08509

Haar Nuclear Norms with Applications to Remote Sensing Imagery Restoration

Shuang Xu·Chang Yu·Jiangjun Peng·Xiangyong Cao·Deyu Meng
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Abstract

Remote sensing image restoration aims to reconstruct missing or corrupted areas within images. To date, low-rank based models have garnered significant interest in this field. This paper proposes a novel low-rank regularization term, named the Haar nuclear norm (HNN), for efficient and effective remote sensing image restoration. It leverages the low-rank properties of wavelet coefficients derived from the 2-D frontal slice-wise Haar discrete wavelet transform, effectively modeling the low-rank prior for separated coarse-grained structure and fine-grained textures in the image. Experimental evaluations conducted on hyperspectral image inpainting, multi-temporal image cloud removal, and hyperspectral image denoising have revealed the HNN's potential. Typically, HNN achieves a performance improvement of 1-4 dB and a speedup of 10-28x compared to some state-of-the-art methods (e.g., tensor correlated total variation, and fully-connected tensor network) for inpainting tasks.

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Shuang Xu, Chang Yu, Jiangjun Peng, Xiangyong Cao, Deyu Meng. 2024-07-11. Haar Nuclear Norms with Applications to Remote Sensing Imagery Restoration. https://arxiv.org/abs/2407.08509

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