Different models for estimating depth from defocused images have been proposed over the years. Typically two differently defocused images are used by these models. Many of them work on the principle of transforming one or both of the images so that the transformed images become equivalent. One of the most common models is to estimate the relative blur between a pair of defocused images and compute depth from it. Another model known as the Blur Equalization Technique (BET) works by blurring both images by an appropriate pair of blur kernels. The inverse approach is to deblur both images by an appropriate pair of blur kernels. In this paper we compare the performance of these models to find under what conditions they work best. We show that the common approach of using the Gaussian approximation of the relative blur kernel performs worse than a more general approximation of the relative blur kernel. Furthermore, we show that despite the reduction in signal content in BET, it works well in most circumstances. Finally, the performance of deconvolution based approaches depends on a large part on the shape of the blur kernel and is more appropriate for the coded aperture setup.