Coded Aperture Snapshot Spectral Imaging (CASSI) system has great advantages
over traditional methods in dynamically acquiring Hyper-Spectral Image (HSI),
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 designs the AMDC-CASSI system, introducing
RGB camera with CASSI based on Adaptive-Mask as multimodal input to improve the
reconstruction quality. The existing SOTA reconstruction schemes are based on
transformer, but the operation of self-attention pulls down the operation
efficiency of the network. In order 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 a 8 db improvement over SOTA and at least a 5-fold
improvement in reconstruction speed. (this https URL)

本研究介绍了基于自适应掩模的多模输入和用于高光谱成像重建的 MLP-AMDC 结构的改进 CASSI 系统，该方法在重建速度和性能上比现有方案有明显的改进。

MLP-AMDC：一种用于自适应掩膜双摄像头快照高光谱成像的 MLP 架构

MLP-AMDC: An MLP Architecture for Adaptive-Mask-based Dual-Camera  snapshot hyperspectral imaging

In coded aperture snapshot spectral imaging (CASSI) system, the real-world
hyperspectral image (HSI) can be reconstructed from the captured compressive
image in a snapshot. Model-based HSI reconstruction methods employed
hand-crafted priors to solve the reconstruction problem, but most of which
achieved limited success due to the poor representation capability of these
hand-crafted priors. Deep learning based methods learning the mappings between
the compressive images and the HSIs directly achieved much better results. Yet,
it is nontrivial to design a powerful deep network heuristically for achieving
satisfied results. In this paper, we propose a novel HSI reconstruction method
based on the Maximum a Posterior (MAP) estimation framework using learned
Gaussian Scale Mixture (GSM) prior. Different from existing GSM models using
hand-crafted scale priors (e.g., the Jeffrey's prior), we propose to learn the
scale prior through a deep convolutional neural network (DCNN). Furthermore, we
also propose to estimate the local means of the GSM models by the DCNN. All the
parameters of the MAP estimation algorithm and the DCNN parameters are jointly
optimized through end-to-end training. Extensive experimental results on both
synthetic and real datasets demonstrate that the proposed method outperforms
existing state-of-the-art methods. The code is available at
this https URL

本文提出了一种新颖的基于深度学习和高斯混合先验的 Maximum a Posteriori (MAP) 估计框架的高光谱成像重建方法，该方法通过深度卷积神经网络 (DCNN) 学习尺度先验和局部均值参数，在合成和真实数据集上均取得了超过现有前沿方法的最佳结果。