Non-line-of-sight imaging algorithm based on Wiener filtering of mid-frequency

Abstract

Non-line-of-sight (NLOS) imaging is an emerging technology for optically imaging the objects blocked beyond the detector's line of sight. The NLOS imaging based on light-cone transform and inverted method can be regarded as a deconvolution process. The traditional Wiener filtering deconvolution method uses the empirical values or the repeated attempts to obtain the power spectral density noise-to-signal ratio (PSDNSR) of the transient image: each hidden scene has a different PSDNSR for NLOS imaging, so the prior estimation is not appropriate and repeated attempts make it difficult to quickly find the optimal value. Therefore, in this work proposed is a method of estimating the PSDNSR by using the mid-frequency information of captured transient images for Wiener filtering to achieve NLOS imaging. In this method, the turning points between the mid-frequency domain and the high-frequency domain of the transient image amplitude spectrum are determined, and then the PSDNSR value is solved by analyzing the characteristics and relationship among the noise power spectra at the low, middle and high frequency. Experiments show that the PSDNSR estimated by NLOS imaging algorithm based on Wiener filtering of mid-frequency domain has a better reconstruction effect. Compared with other methods, the algorithm in this work can directly estimate PSDNSR in one step, without iterative operations, and the computational complexity is low, therebysimplifying the parameter adjustment steps of the Wiener filtering deconvolution NLOS imaging algorithm based on light-cone transform. Therefore the reconstruction efficiency can be improved on the premise of ensuring the reconstruction effect.