The behavior of dense colloidal fluids near surfaces can now be probed in great detail with experimental techniques like video and confocal microscopy. In fact we are approaching a point where quantitative comparisons of experiments with particle-level theory, such as classical density functional theory (DFT), are appropriate. In a forward sense, we may use a known surface potential to predict a particle density distribution function from DFT; in an inverse sense, we may use an experimentally measured particle density distribution function to predict the underlying surface potential from DFT. In this dissertation, we tested the ability of the closure-based DFT to perform forward and inverse calculations on potential models commonly employed for colloidal particles and surface under different surface topographies. To reduce sources of uncertainty in this initial study, Monte Carlo simulation results played the role of experimental data. The accuracy of the predictions depended on the bulk particle density, potential well depth and the choice of DFT closure relationships. For a reasonable range of choices of the density, temperature, potential parameters, and surface features, the inversion procedure yielded particle-surface potentials to an accuracy on the order of 0.1 kBT. Our results demonstrated that DFT is a valuable numerical tool for microcopy experiments to image three-dimensional surface energetic landscape accurately and rapidly. B