Traditional imaging systems such as the human eye and optical cameras capture the scene ahead of them called the line of sight (LoS) objects. These imaging systems are limited by their lack of field of view (FoV). Information about the non-line of sight (NLoS) objects is lost due to the objects in the LoS. They are either opaque or absorb all the incident energy, allowing for no information about the NLoS scene to be transmitted back to the detector. Amongst the popular methods used for NLoS imaging, acoustic imaging [1] offers low resolutions and suffers from interference from environmental factors. Optical methods like time-of-flight (ToF) imaging perform poorly due to shorter wavelengths leading to more scattering and absorption by occluding objects in the scene. NLoS imaging with electromagnetic (EM) rays is preferred over traditional methods because of its allowance for higher spatial resolution. It is subject to lesser interference by atmospheric factors (wind, temperature gradients.)Most everyday surfaces offer diffuse and specular reflection due to their material properties. They behave as lossy mirrors enabling propagation paths between a Terahertz (THz) Imaging System and the NLoS objects. THz waves (300 GHz – 10 THz) are the least explored if not exploited band of frequencies in the EM spectrum. A THz NLoS Imaging system is a Radar (Radio Detection and Ranging) that works by recording the backscatter information received from sending out EM signals into free space where the EM signals undergo multiple bounces off different objects in the scene. Due to the inherent nature of the radars, the return information is perceived in a way that the NLoS objects are improperly depicted when reconstructed. A correction algorithm to account for this misplacement in the reconstruction of NLoS images is proposed and its implementation is discussed in detail as a part of this work. The reconstruction algorithm processes the obtained raw THz image and performs multiple stages of classification between LoS and NLoS objects using ray casting [2]. Then the information about line-of-sight objects is fed to a line detection mechanism to detect and model the detected surfaces as mirrors. Mirror folding [3] is performed starting from the farthest generations for the objects in non-line of sight. This algorithm has been evaluated with simulated images of objects behind a single wall and two walls. With the help of a scanning THz imaging system, measurements were collected in a controlled environment, and this data was fed into the implemented algorithm for testing.