Abstract
Single-pixel imaging is a promising technique for Terahertz (THz) imaging, because it can largely reduce the numbers of expensive THz emitter and detector. Single-pixel imaging uses compressive sensing (CS) to measure the compressed THz signal and to recover the object image. This work presents a low-complexity and high-quality total variation augmented Lagrangian method (2D-TVALM) algorithm based on the well-known total variation augmented Lagrangian alternating-direction algorithm (TVAL3). TVAL3 is a convex optimization algorithm used to solve the problem of total variation (TV) regularization. By designing the sampling matrix and some mathematic approximation, this work largely reduces the computational complexity and speed up the convergence. The simulation results show that the reconstruction quality of the proposed algorithm is about 7 dB higher than TVAL3 in PSNR and the performance is much better than that of the other CS algorithms. In the computation time, the proposed algorithm is about 31 times faster than TVAL3. Finally, the average PSNR and elapsed time of the proposed algorithm are 38.43 dB and 0.0558 s respectively with 200 iterations in the 15 test patterns.