用图神经网络压缩太阳多光谱图像，更保真更清晰。
Spectral and Spatial Graph Learning for Multispectral Solar Image Compression
- 将光谱通道建模为图节点，学习跨波段关系，提升细节保留。
- 结合图注意力与卷积注意力，减少空间冗余，增强细结构。
- 在六波段太阳图像上显著提升保真度，适合天文数据压缩。
高保真多光谱太阳图像压缩对空间任务仍具挑战性，受限带宽需兼顾精细光谱与空间细节的保留。本文提出一种专为太阳观测设计的端到端学习压缩框架，包含两个互补模块：(1) 跨光谱窗式图嵌入（iSWGE），将各光谱波段视为具有可学习边特征的图节点，显式建模波段间关系；(2) 窗式空间图注意力与卷积注意力块（WSGA-C），通过稀疏图注意力与卷积注意力结合，降低空间冗余并强调细尺度结构。在包含六条极紫外（EUV）通道的SDOML数据集上的评估显示，该方法相较强基线实现20.15%的均光谱信息散度（MSID）降低，峰值信噪比（PSNR）最高提升1.09%，对数变换后的MS-SSIM提升1.62%，在相近比特率下实现更锐利、更光谱忠实的重建结果。代码已开源：https://github.com/agyat4/sgraph。
原文摘要 · Abstract (English)
High-fidelity compression of multispectral solar imagery remains challenging for space missions, where limited bandwidth must be balanced against preserving fine spectral and spatial details. We present a learned image compression framework tailored to solar observations, leveraging two complementary modules: (1) the Inter-Spectral Windowed Graph Embedding (iSWGE), which explicitly models inter-band relationships by representing spectral channels as graph nodes with learned edge features; and (2) the Windowed Spatial Graph Attention and Convolutional Block Attention (WSGA-C), which combines sparse graph attention with convolutional attention to reduce spatial redundancy and emphasize fine-scale structures. Evaluations on the SDOML dataset across six extreme ultraviolet (EUV) channels show that our approach achieves a 20.15%reduction in Mean Spectral Information Divergence (MSID), up to 1.09% PSNR improvement, and a 1.62% log transformed MS-SSIM gain over strong learned baselines, delivering sharper and spectrally faithful reconstructions at comparable bits-per-pixel rates. The code is publicly available at https://github.com/agyat4/sgraph .
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