Hyperspectral image (HSI) denoising is essentially ill-posed since a noisy HSI can be degraded from multiple clean HSIs. However, existing deep learning (DL)-based approaches only restore one clean HSI from the given noisy HSI with a deterministic mapping, thus ignoring the ill-posed issue and always resulting in an over-smoothing problem. Additionally, these DL-based methods often neglect that noise is part of the high-frequency component and their network architectures fail to decouple the learning of low-frequency and high-frequency. To alleviate these issues, 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 generative flow model and is comprised of an invertible decoder and a conditional encoder, which can explicitly 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. 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 experiments on simulated and real HSI datasets verify that our proposed HIDFlowNet can obtain better or comparable results compared with other state-of-the-art methods.

翻译：高光谱图像（HSI）去噪本质上是一个不适定问题，因为一个含噪HSI可能由多个干净HSI退化而来。然而，现有的基于深度学习（DL）的方法仅通过确定性映射从给定的含噪HSI中恢复出一个干净HSI，从而忽略了不适定性问题，并总是导致过度平滑现象。此外，这些基于深度学习的方法往往忽视了噪声属于高频分量的一部分，且其网络架构未能解耦低频与高频的学习。为缓解这些问题，本文提出一种基于流的HSI去噪网络（HIDFlowNet），直接学习给定含噪HSI条件下干净HSI的条件分布，从而可以从该条件分布中采样得到多样化的干净HSI。总体而言，我们的HIDFlowNet由生成流模型推导而来，包含一个可逆解码器和一个条件编码器，能够显式地解耦HSI低频与高频信息的学习。具体而言，可逆解码器通过堆叠一系列可逆条件块（ICB）构建，以捕捉局部高频细节。条件编码器利用下采样操作获取低分辨率图像，并采用transformer捕获长距离相关性，从而有效提取全局低频信息。在模拟和真实HSI数据集上的大量实验验证了所提出的HIDFlowNet相较于其他先进方法能够取得更好或相当的结果。