Pixel-Based Long-Wave Infrared Spectral Image Reconstruction Using a Hierarchical Spectral Transformer.

Long-wave infrared (LWIR) spectral imaging plays a critical role in various applications such as gas monitoring, mineral exploration, and fire detection. Recent advancements in computational spectral imaging, powered by advanced algorithms, have enabled the acquisition of high-quality spectral images in real time, such as with the Uncooled Snapshot Infrared Spectrometer (USIRS). However, the USIRS system faces challenges, particularly a low spectral resolution and large amount of data noise, which can degrade the image quality. Deep learning has emerged as a promising solution to these challenges, as it is particularly effective at handling noisy data and has demonstrated significant success in hyperspectral imaging tasks. Nevertheless, the application of deep learning in LWIR imaging is hindered by the severe scarcity of long-wave hyperspectral image data, which limits the training of robust models. Moreover, existing networks that rely on convolutional layers or attention mechanisms struggle to effectively capture both local and global spectral correlations. To address these limitations, we propose the pixel-based Hierarchical Spectral Transformer (HST), a novel deep learning architecture that learns from publicly available single-pixel long-wave infrared spectral databases. The HST is designed to achieve a high spectral resolution for LWIR spectral image reconstruction, enhancing both the local and global contextual understanding of the spectral data. We evaluated the performance of the proposed method on both simulated and real-world LWIR data, demonstrating the robustness and effectiveness of the HST in improving the spectral resolution and mitigating noise, even with limited data.