Cost-efficient Entangled Light Quantum Imaging Based on Compressed Sensing

Abstract

A cost-efficient entangled light quantum imaging method is proposed to improve imaging speed while preserve imaging quality. First of all, the target image is sparsely processed by wavelet transform, and the random Bernoulli matrix is selected as the measurement matrix to ensure the retention of the effective information in the sparse signal. Then, the image is reconstructed by using the Total Variation Augmented Lagrangian Alternating Direction Algorithm (TVAL3) to improve reconstruction accuracy and computation efficiency. Finally, the reconstruction quality of images at different sampling rates is compared and analyzed, and the effectiveness of the proposed method is verified based on an actual optical path of entangled light quantum imaging.