Single-Pixel Imaging Based on Deep Learning Enhanced Singular Value Decomposition.

We propose and demonstrate a single-pixel imaging method based on deep learning network enhanced singular value decomposition. The theoretical framework and the experimental implementation are elaborated and compared with the conventional methods based on Hadamard patterns or deep convolutional autoencoder network. Simulation and experimental results show that the proposed approach is capable of reconstructing images with better quality especially under a low sampling ratio down to 3.

High-efficiency terahertz single-pixel imaging based on a physics-enhanced network.

As an alternative solution to the lack of cost-effective multipixel terahertz cameras, terahertz single-pixel imaging that is free from pixel-by-pixel mechanical scanning has been attracting increasing attention. Such a technique relies on illuminating the object with a series of spatial light patterns and recording with a single-pixel detector for each one of them. This leads to a trade-off between the acquisition time and the image quality, hindering practical applications.

Automatic recognition of breast invasive ductal carcinoma based on terahertz spectroscopy with wavelet packet transform and machine learning.

We demonstrate an automatic recognition strategy for terahertz (THz) pulsed signals of breast invasive ductal carcinoma (IDC) based on a wavelet entropy feature extraction and a machine learning classifier. The wavelet packet transform was implemented into the complexity analysis of the transmission THz signal from a breast tissue sample. A novel index of energy to Shannon entropy ratio (ESER) was proposed to distinguish different tissues.