Abstract
Hyperspectral imaging (HSI) is essential across various disciplines for its capacity to capture rich spectral information. However, efficiently reconstructing hyperspectral images from compressive sensing measurements presents significant challenges. To tackle these, we adopt a divide-and-conquer strategy that capitalizes on the unique spectral and spatial characteristics of hyperspectral images. We introduce the Lightweight Separate Spectral Transformer (LSST), an innovative architecture tailored for efficient hyperspectral image reconstruction. This architecture consists of Separate Spectral Transformer Blocks (SSTB) for modeling spectral relationships and Lightweight Spatial Convolution Blocks (LSCB) for spatial processing. The SSTB employs Grouped Spectral Self-attention and a Spectrum Shuffle operation to effectively manage both local and non-local spectral relationships. Simultaneously, the LSCB utilizes depth-wise separable convolutions and strategic ordering to enhance spatial information processing. Furthermore, we implement the Focal Spectrum Loss, a novel loss weighting mechanism that dynamically adjusts during training to improve reconstruction across spectrally complex bands. Extensive testing demonstrates that our LSST achieves superior performance while requiring fewer FLOPs and parameters, underscoring its efficiency and effectiveness. The source code is available at: this https URL.
Abstract (translated)
高光谱成像（HSI）因其能够捕获丰富的光谱信息而在多个学科中至关重要。然而，从压缩感知测量高效重建高光谱图像面临重大挑战。为解决这些问题，我们采用了一种分而治之的策略，利用了高光谱图像独特的光谱和空间特性。为此，我们引入了轻量级独立光谱变压器（LSST），这是一种创新架构，专为高效的高光谱图像重建设计。 该架构包括用于建模光谱关系的独立光谱变换模块（SSTB）以及用于处理空间信息的轻量化空间卷积块（LSCB）。SSTB 采用分组光谱自注意力机制和光谱混洗操作，有效管理局部和非局部光谱关系。同时，LSCB 使用深度可分离卷积并采取战略性排列以增强空间信息处理。 此外，我们实施了焦点光谱损失（Focal Spectrum Loss），这是一种新颖的损失加权机制，在训练过程中动态调整，旨在改善复杂光谱带中的重建效果。广泛的测试表明，我们的 LSST 实现了卓越的性能，同时需要较少的浮点运算数（FLOPs）和参数，突显其高效性和有效性。源代码可在以下网址获取：[提供链接的地方，请根据实际情况插入正确的URL]。
URL
https://arxiv.org/abs/2601.01064
