Microgrid polarimeters are a type of division of focal plane (DoFP) imaging polarimeter that contains a mosaic of pixel-wise micropolarizing elements superimposed upon an FPA sensor. Such a device measures a slightly different polarized state at each pixel. These measurements are combined to estimate the Stokes vector at each pixel in the image. DoFP devices have the advantage that they can obtain Stokes vector image estimates for an entire scene from a single frame capture. However, they suffer from the disadvantage that the neighboring measurements that are used to estimate the Stokes vector images are acquired at differing instantaneous fields of view (IFOV). This IFOV issue leads to false polarization signatures that significantly degrade the Stokes vector images. Interpolation and other image processing strategies can be employed to reduce IFOV artifacts; however these techniques have a limit to the amount of enhancement they can provide on a single microgrid image. Here we investigate algorithms that use multiple microgrid images that contain frame-to-frame global motion to further enhance the Stokes vector image estimates. Motion-based imagery provides additional redundancy that can be exploited to recover information that is "missing" from a single microgrid frame capture. We have found that IFOV and aliasing artifacts can be defeated entirely when these types of algorithms are applied to the data prior to Stokes vector estimation. We demonstrate results on real LWIR microgrid data using a particular resolution enhancement technique from the literature.