Due to inadequate energy captured by the hyperspectral camera sensor in poor illumination conditions, low-light hyperspectral images (LHSIs) usually suffer from low visibility, spectral distortion, and various noises. A range of hyperspectral image (HSI) restoration methods have been developed, yet their effectiveness in enhancing low-light HSIs is constrained. This work focuses on the low-light HSI enhancement task, which aims to reveal the spatial–spectral information hidden in darkened areas. To facilitate the development of low-light HSI processing, we collect an LHSI dataset of both indoor and outdoor scenes. Based on Laplacian pyramid decomposition and reconstruction, we developed an end-to-end data-driven low-light HSI enhancement (HSIE) approach trained on the LHSI dataset. With the observation that illumination is related to the low-frequency component of HSI, while textural details are closely correlated with the high-frequency component, the proposed HSIE is designed to have two branches. The illumination enhancement branch is adopted to enlighten the low-frequency component with reduced resolution. The high-frequency refinement branch is utilized for refining the high-frequency component via a predicted mask. In addition, to improve information flow and boost performance, we introduce an effective channel attention block (CAB) with residual dense connection, which served as the basic block of the illumination enhancement branch. The effectiveness and efficiency of HSIE both in quantitative assessment measures and visual effects are demonstrated by experimental results on the LHSI dataset. According to the classification performance on the remote-sensing Indian Pines dataset, downstream tasks benefit from the enhanced HSI. Datasets and codes are available at https://github.com/guanguanboy/HSIE.

中文翻译：

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低光高光谱图像增强


由于高光谱相机传感器在恶劣的照明条件下捕获的能量不足，低光高光谱图像（LHSI）通常会出现可见度低、光谱失真和各种噪声的问题。一系列高光谱图像 (HSI) 恢复方法已经被开发出来，但它们在增强低光 HSI 方面的有效性受到限制。这项工作重点关注低光 HSI 增强任务，旨在揭示隐藏在黑暗区域中的空间光谱信息。为了促进低光 HSI 处理的开发，我们收集了室内和室外场景的 LHSI 数据集。基于拉普拉斯金字塔分解和重建，我们开发了一种在 LHSI 数据集上训练的端到端数据驱动的低光 HSI 增强 (HSIE) 方法。根据观察，光照与 HSI 的低频分量相关，而纹理细节与高频分量密切相关，因此所提出的 HSIE 被设计为具有两个分支。采用照明增强分支来照明分辨率降低的低频分量。高频细化分支用于通过预测掩模来细化高频分量。此外，为了改善信息流并提高性能，我们引入了具有残差密集连接的有效通道注意块（CAB），它作为照明增强分支的基本块。 LHSI 数据集上的实验结果证明了 HSIE 在定量评估措施和视觉效果方面的有效性和效率。根据遥感 Indian Pines 数据集的分类性能，下游任务受益于增强的 HSI。 数据集和代码可在 https://github.com/guanguanboy/HSIE 获取。