  Hyperspectral image (HSI) denoising is essentially ill-posed since a noisy
HSI can be degraded from multiple clean HSIs. However, current deep
learning-based approaches ignore this fact and restore the clean image with
deterministic mapping (i.e., the network receives a noisy HSI and outputs a
clean HSI). To alleviate this issue, this paper proposes a flow-based HSI
denoising network (HIDFlowNet) to directly learn the conditional distribution
of the clean HSI given the noisy HSI and thus diverse clean HSIs can be sampled
from the conditional distribution. Overall, our HIDFlowNet is induced from the
flow methodology and contains an invertible decoder and a conditional encoder,
which can fully decouple the learning of low-frequency and high-frequency
information of HSI. Specifically, the invertible decoder is built by staking a
succession of invertible conditional blocks (ICBs) to capture the local
high-frequency details since the invertible network is information-lossless.
The conditional encoder utilizes down-sampling operations to obtain
low-resolution images and uses transformers to capture correlations over a long
distance so that global low-frequency information can be effectively extracted.
Extensive experimental results on simulated and real HSI datasets verify the
superiority of our proposed HIDFlowNet compared with other state-of-the-art
methods both quantitatively and visually.
