Non-line-of-sight object detection based on the Orthogonal Matching Pursuit Compressive Sensing Reconstruction

Recently, non-line-of-sight (NLOS) imaging is a novel optical computational imaging technology developed. In NLOS imaging, objects are reconstructed by analyzing information carried in the reflected light. Considering the flaws in NLOS such as information loss and weak target detection due to diffuse reflection of light, developing a stable and effective imaging system is nontrivial. Therefore, in this paper, a 2D object inspection system based on compressed sensing and Orthogonal Matching Pursuit (CSOMP) approaches for effective NLOS imaging is proposed. This will not only alleviate the difficulty in collecting object information in a low light environment but also make the reconstruction process with a few measurements. Specifically, the single-pixel camera based on Digital Micromirror Device (DMD) is introduced to fully exploit the programmable pattern information and the correlation between the measured intensity data for image renovation. To reduce acquisition time, a high-efficiency single-pixel detector is employed that obviates the need for mechanical scanning. Also, in the image reconstruction stage, the residuals of the measurement matrix are obtained through OMP. The proposed method combines with the CS theory; thus, the target can be rebuilt with fewer measurements. Experimental results on NLOS images illustrate that the proposed imaging scheme reconstructed the image exactly similar to the real object.