HIR-Diff: Unsupervised Hyperspectral Image Restoration Via Improved Diffusion Models
<|reference_start|>HIR-Diff: Unsupervised Hyperspectral Image Restoration Via Improved Diffusion Models: Hyperspectral image (HSI) restoration aims at recovering clean images from degraded observations and plays a vital role in downstream tasks. Existing model-based methods have limitations in accurately modeling the complex image characteristics with handcraft priors, and deep learning-based methods suffer from poor generalization ability. To alleviate these issues, this paper proposes an unsupervised HSI restoration framework with pre-trained diffusion model (HIR-Diff), which restores the clean HSIs from the product of two low-rank components, i.e., the reduced image and the coefficient matrix. Specifically, the reduced image, which has a low spectral dimension, lies in the image field and can be inferred from our improved diffusion model where a new guidance function with total variation (TV) prior is designed to ensure that the reduced image can be well sampled. The coefficient matrix can be effectively pre-estimated based on singular value decomposition (SVD) and rank-revealing QR (RRQR) factorization. Furthermore, a novel exponential noise schedule is proposed to accelerate the restoration process (about 5$\times$ acceleration for denoising) with little performance decrease. Extensive experimental results validate the superiority of our method in both performance and speed on a variety of HSI restoration tasks, including HSI denoising, noisy HSI super-resolution, and noisy HSI inpainting. The code is available at https://github.com/LiPang/HIRDiff.<|reference_end|>
@article{pang2024hir-diff:,
    title={HIR-Diff: Unsupervised Hyperspectral Image Restoration Via Improved
  Diffusion Models},
    author={Li Pang, Xiangyu Rui, Long Cui, Hongzhong Wang, Deyu Meng, Xiangyong
  Cao},
    journal={arXiv preprint arXiv:2402.15865},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.15865},
    primaryClass={cs.CV eess.IV}
}