HyperDecouple_Net: A Decoupling Algorithm for Crosstalk in 2D Spectral Images

This paper addresses the imaging crosstalk problem in 2D spectra from the LAMOST Phase II upgrade, caused by increased fiber density. We propose HyperDecouple_Net, a hypernetwork-based decoupling algorithm designed to overcome key limitations of existing deep learning models, including overlapping-layer collapse and structural distortion. The method integrates an adaptive overlapping-layer enhancement module, a dual-scale hypernetwork differential decoupling module, and a linear consistency constraint module. Additionally, we introduce LAMOST-SD-2026, a public dataset comprising 15,500 linearly superimposed spectral samples with ground-truth labels, derived from real LAMOST Phase I observations. Experimental results on this dataset show that HyperDecouple_Net achieves superior performance, with a PSNR_A of 12.71 dB, PSNR_B of 10.87 dB, SSIM_B of 0.3895, and SAM of 0.4841, outperforming both traditional methods (e.g., NMF, ICA) and recent deep learning approaches. The proposed method can be directly integrated into the LAMOST Phase II preprocessing pipeline, offering a robust solution for high-precision spectral decoupling and supporting the scientific output of the survey.