MLP-AMDC: A MLP Architecture for Adaptive-Mask-Based Dual-Camera Snapshot Hyperspectral Imaging

Zeyu Cai, Can Zhang, Yuchong Chen, Xunhao Chen, Jiming Yang, Wubin Shi, Feipeng Da, Chengqian Jin

Abstract: The coded Aperture Snapshot Spectral Imaging (CASSI) system has great advantages in dynamically acquiring Hyper-Spectral Image (HSI) compared to traditional measurement methods, but there are the following problems. 1) Traditional mask relies on random patterns or analytical design, both of which limit the performance improvement of CASSI. 2) Existing high-quality reconstruction algorithms are slow in reconstruction and can only reconstruct scene information offline. To address the above two problems, this paper introduces an RGB camera with CASSI based on Adaptive-Mask as multimodal input to improve the reconstruction quality. The existing SOTA reconstruction schemes are based on the transformer, but the operation of self-attention pulls down the operation efficiency of the network. To improve the inference speed of the reconstruction network, this paper proposes An MLP Architecture for Adaptive-Mask-based Dual-Camera (MLP-AMDC) to replace the transformer structure of the network. Numerous experiments have shown that MLP performs no less well than transformer-based structures for HSI reconstruction, while MLP greatly improves the network inference speed and has less number of parameters and operations, our method has an 8 dB improvement over SOTA and at least a 5-fold improvement in reconstruction speed. (https://github.com/caizeyu1992/MLP-AMDC.)