Deep learning models are state-of-the-art in compressive spectral imaging (CSI) recovery. These methods use a deep neural network (DNN) as an image generator to learn non-linear mapping from compressed measurements to the spectral image. For instance, the deep spectral prior approach uses a convolutional autoencoder network (CAE) in the optimization algorithm to recover the spectral image by using a non-linear representation. However, the CAE training is detached from the recovery problem, which does not guarantee optimal representation of the spectral images for the CSI problem. This work proposes a joint non-linear representation and recovery network (JR2net), linking the representation and recovery task into a single optimization problem. JR2net consists of an optimization-inspired network following an ADMM formulation that learns a non-linear low-dimensional representation and simultaneously performs the spectral image recovery, trained via the end-to-end approach. Experimental results show the superiority of the proposed method with improvements up to 2.57 dB in PSNR and performance around 2000 times faster than state-of-the-art methods.

翻译：深度学习模型是压缩光谱成像(CSI)恢复方面最先进的先进模型。这些方法使用深神经网络(DNN)作为图像生成器,学习从压缩测量到光谱图像的非线性绘图;例如,深光先行方法在优化算法中使用进动自动编码网络(CAE),以便使用非线性表示法恢复光谱图像;然而,CAE培训与恢复问题脱钩,这并不能保证CSI问题的光谱图像的最佳表达方式。这项工作提议建立一个非线性表示法和恢复联合网络(JR2net),将代表制和恢复任务与单一优化问题联系起来。JR2net在ADMM制配方后由优化型网络组成,通过非线性低度表示法学习非线性低度表示法,同时进行光谱图像恢复,通过端对端至端方法进行培训。实验结果显示拟议方法的优越性,PSNR改进至2.57 dB,2000年左右的性能速度比州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州-州