Learning-Based THz Multi-Layer Imaging With Model-Based Masks

This paper demonstrates a learning-based THz multi-layer pixel identification for non-destructive inspection. Specifically, we introduce a recurrent neural network that sequentially learns features from THz spectrogram segments with masks from model-based sparse deconvolution. Initial performance evaluation on a three-layer sample with contents on all surfaces confirms the effectiveness of the proposed method.