HIR-Diff: Unsupervised Hyperspectral Image Restoration Via Improved
  Diffusion Models
Li Pang Xiangyu Rui Long Cui Hongzhong Wang Deyu Meng Xiangyong Cao
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.