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 https://see.xidian.edu.cn/faculty/wsdong/Projects/DGSM-SCI.htm.
翻译:在代码孔径光速光谱成像系统(CASSI)中,真实世界超光谱图像(HSI)可以从所捕捉的压缩图像中以快照形式重建。基于模型的HSI重建方法采用了手工制作的前期方法来解决重建问题,但由于这些手工制作的前科的演示能力差,其中多数取得了有限的成功。深层次学习基于学习的方法在压缩图像和高光谱成像之间的绘图直接取得更好的结果。然而,设计一个强大的深深网络超光谱图像(HSI),以便取得令人满意的结果,并不值得一试。在本文中,我们提议根据最大波斯内面(MAP)估算框架采用新的HSI重建方法。所有参数都不同于现有手工制作前级(例如,Jeffrey的前期)的现有GSM模型模型模型模型,我们提议通过深层的转动神经神经网络(DCNNN)来学习比例。此外,我们还提议由DCNNNNE(D)估算GS模型的当地手段。所有参数都是通过Gosimal-S-imalimalimal ad amal dassal dassal 和DMISal 正在展示现有的最新数据分析法,这是在目前进行最佳的模拟分析和DMISDMLADMLA和DMLA和DADADADADADA和DSDS的模型中的拟议最佳的现有数据分析方法,这是现有的模拟方法。我们提议的现有的模型。