Research Paper#Image Compression, Graph Neural Networks, Solar Imagery🔬 ResearchAnalyzed: Jan 3, 2026 06:32
Solar Image Compression with Spectral and Spatial Graph Learning
Published:Dec 30, 2025 20:54
•1 min read
•ArXiv
Analysis
This paper addresses the challenge of compressing multispectral solar imagery for space missions, where bandwidth is limited. It introduces a novel learned image compression framework that leverages graph learning techniques to model both inter-band spectral relationships and spatial redundancy. The use of Inter-Spectral Windowed Graph Embedding (iSWGE) and Windowed Spatial Graph Attention and Convolutional Block Attention (WSGA-C) modules is a key innovation. The results demonstrate significant improvements in spectral fidelity and reconstruction quality compared to existing methods, making it relevant for space-based solar observations.
Key Takeaways
Reference
“The 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.”