Visualization and analysis of pulsed thermographic data for NDT has generally been based on simple image averaging, subtraction or slope operations. Quantitative contrast methods, based on comparison to a defect free reference point or region, have also been used to a lesser extent. Despite their widespread use, all of these methods are highly susceptible to noise, nonlinearity of the IR camera response, and the presence of surface features on the sample. More importantly, the ability of any of these methods to significantly improve the ability to retrieve deep or weak subsurface features beyond the original unmodified image is limited. In a previous paper, we introduced the concept of Thermographic Signal Reconstruction (TSR) as a means of enhancing defect to background contrast while reducing the amount of data that must be stored by an order of magnitude. The TSR method increases the depth range over which pulsed thermography can be applied, and also reduces the amount of blurring due to lateral diffusion that is typical of thermographic imaging. In this paper we compare TSR with conventional thermographic approaches and consider the mechanisms for the resulting performance improvements.