Hyperspectral dehazing (HyDHZ) has become a crucial signal processing technology to facilitate the subsequent identification and classification tasks, as the airborne visible/infrared imaging spectrometer (AVIRIS) data portal reports a massive portion of haze-corrupted areas in typical hyperspectral remote sensing images. The idea of inverse problem transform (IPT) has been proposed in recent remote sensing literature in order to reformulate a hardly tractable inverse problem (e.g., HyDHZ) into a relatively simple one. Considering the emerging spectral super-resolution (SSR) technique, which spectrally upsamples multispectral data to hyperspectral data, we aim to solve the challenging HyDHZ problem by reformulating it as an SSR problem. Roughly speaking, the proposed algorithm first automatically selects some uncorrupted/informative spectral bands, from which SSR is applied to spectrally upsample the selected bands in the feature space, thereby obtaining a clean hyperspectral image (HSI). The clean HSI is then further refined by a deep transformer network to obtain the final dehazed HSI, where a global attention mechanism is designed to capture nonlocal information. There are very few HyDHZ works in existing literature, and this article introduces the powerful spatial-spectral transformer into HyDHZ for the first time. Remarkably, the proposed transformer-driven IPT-based HyDHZ (T2HyDHZ) is a blind algorithm without requiring the user to manually select the corrupted region. Extensive experiments demonstrate the superiority of T2HyDHZ with less color distortion.

翻译：高光谱去雾（HyDHZ）已成为促进后续识别与分类任务的关键信号处理技术，因为机载可见光/红外成像光谱仪（AVIRIS）数据门户报告显示典型高光谱遥感图像中存在大量雾霾污染区域。近年来遥感文献中提出的逆问题变换（IPT）思想，旨在将难以处理的逆问题（如HyDHZ）重构为相对简单的问题。考虑到新兴的光谱超分辨率（SSR）技术可将多光谱数据在光谱维度上采样至高光谱数据，我们尝试通过将HyDHZ问题重构为SSR问题来解决这一挑战性任务。简而言之，所提算法首先自动选择未受污染/信息丰富的谱段，在特征空间中对选定谱段进行光谱上采样的SSR处理，从而获得洁净的高光谱图像（HSI）。随后通过深度Transformer网络进一步优化洁净HSI以得到最终去雾结果，其中设计的全局注意力机制能够捕捉非局部信息。现有文献中关于HyDHZ的研究极少，本文首次将强大的空谱Transformer引入HyDHZ领域。值得注意的是，所提出的基于Transformer驱动IPT的HyDHZ方法（T2HyDHZ）是无需用户手动选择污染区域的盲算法。大量实验证明T2HyDHZ在减少色彩失真方面具有优越性。