Photon-limited depth and reflectivity imaging with sparsity regularization

Abstract

We demonstrate a depth and reflectivity imaging system at low light level based on sparsity regularization method. Depth and reflectivity imaging from the time-correlated single photon counting (TCSPC) measurement in limit of few photon counts are reconstructed through exploiting transform-domain sparsity. Two different sparsity-based penalty function: total variation (TV) penalty and l1 norm penalty measuring sparsity in the discrete cosine transform(DCT) basis, are applied to the experimental data. The results show that compared with traditional image denoising method, sparsity regularization approach achieves better accuracy with fewer photon measurements. Further more, the performance of TV regularization is proved better than l1-DCT regularization method for photon-limited imaging at first time, especially in the case of depth imaging. Our system is a photon-limited imaging device for a variety of applications, such as target detection, space surveillance, and distance measurement.