Dual-Domain Feature Fusion and Multi-Level Memory-Enhanced Network for Spectral Compressive Imaging

In the Coded Aperture Snapshot Spectral Imaging (CASSI) systems, hyperspectral images (HSIs) reconstruction methods are employed to recover 3D signals from 2D compressive measurements. Among these methods, deep unfolding networks exhibit the benefits of interpretability and high efficiency, but they still have some notable shortcomings. Firstly, existing methods primarily exploit the spatial-spectral domain information of HSIs, neglecting exploration of the frequency domain, which is also beneficial to 3D HSIs. Secondly, current unfolding networks have limited utilization of information between different stages, failing to fully explore their relevance and thereby limiting the effectiveness of the overall framework. To address these issues, in this paper, we propose an integrated framework with dual-domain feature fusion and multi-level memory enhancement. Specifically, the former represents the first attempt to utilize frequency domain information in the feature space of HSIs overcoming the limitation of spatial-spectral domain features and thereby improving the data expression ability of the network by extracting dual-domain features. Simultaneously, our verification experiments also show that the proposed dual-domain feature representation can indeed extract complementary feature information in HSIs. Moreover, the latter aims to use the structural characteristics of the U-Net network to fully extract the correlation of information between different stages by designing a multi-level memory enhancement network. Extensive experimental results on various datasets validate the superiority of the proposed approach in both subjective and objective outcomes. Our proposed method achieves an average of 0.4dB improvement over the best counterpart method. And the code can be obtained from the link: https://github.com/yingyangke/DFFMM.