Cost-efficient Entangled Light Quantum Imaging Based on Compressed Sensing

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.

I. Introduction

Quantum imaging requires a large number of samples if better imaging results are to be obtained, thus increasing the time cost. To address the shortcomings of existing quantum imaging, this paper exploits the entanglement, imprecision, and non-determinism of entangled optical quanta to carry out research on the cost-efficient quantum imaging method based on entangled light to compensate for the shortcomings of traditional quantum imaging, and provide important support for building quantum imaging systems with better imaging quality and faster imaging speed. Compressed sensing is first proposed in the sampling problem and with subsequent research developed into the field of image recovery. With the proposal of compressed sensing and the continuous advancement of related algorithms, it becomes a general trend to use compressed sensing for quantum imaging based on entangled light. The combination of compressed sensing and quantum imaging based on entangled light can effectively reduce the number of samples and greatly shorten the imaging time.