Haar Nuclear Norms with Applications to Remote Sensing Imagery Restoration
<|reference_start|>Haar Nuclear Norms with Applications to Remote Sensing Imagery Restoration: 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.<|reference_end|>
@article{xu2024haar,
    title={Haar Nuclear Norms with Applications to Remote Sensing Imagery
  Restoration},
    author={Shuang Xu and Chang Yu and Jiangjun Peng and Xiangyong Cao},
    journal={arXiv preprint arXiv:2407.08509},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.08509},
    primaryClass={eess.IV cs.CV}
}