The design of imaging systems involves navigating a complex trade space. As a result, many imaging systems employ focal plane arrays with a detector pitch that is insufficient to meet the Nyquist sampling criterion under diffraction-limited imaging conditions. This undersampling may result in aliasing artifacts and prevent the imaging system from achieving the full resolution afforded by the optics. Another potential source of image degradation, especially for long-range imaging, is atmospheric optical turbulence. Optical turbulence gives rise to spatially and temporally varying image blur and warping from fluctuations in the index of refraction along with optical path. Under heavy turbulence, the blurring from the turbulence acts as an anti-aliasing filter, and undersampling does not generally occur. However, under light to moderate turbulence, many imaging systems will exhibit both aliasing artifacts and turbulence degradation. Few papers in the literature have analyzed or addressed both of these degradations together. In this paper, we provide a novel analysis of undersampling in the presence of optical turbulence. Specifically, we provide an optical transfer function analysis that illustrates regimes where aliasing and turbulence are both present, and where they are not. We also propose and evaluate a super-resolution (SR) method for combating aliasing that offers robustness to optical turbulence. The method has a tuning parameter that allows it to transition from traditional diffraction-limited SR, to pure turbulence mitigation with no SR. The proposed method is based on Fusion of Interpolated Frames (FIF) SR, recently proposed by two of the current authors. We quantitatively evaluate the SR method with varying levels of optical turbulence using simulated sequences. We also presented results using real infrared imagery.