Image Super-Resolution (ISR) is employed to generate high-resolution images from low-resolution inputs. However, most current techniques for ISR encounter important challenges such as: (i) the assumption of sufficient training data availability, and (ii) the presumption that target image regions are complete without missing data. To address these practically important challenges, this study applies a lightweight approach termed Fuzzy Rough Feature Selection-based ANFIS Interpolation for ISR, especially on Martian imagery. Feature extraction algorithms are first applied to capture potentially significant features, and population-based search mechanisms are then utilised to perform effective feature selection (via extending the popular fuzzy-rough feature selection mechanism). The selected feature set is subsequently fed into an ANFIS interpolation model to perform the ISR task. Particularly, to handle the issue of sparse and incomplete data in dealing with Mars images, two adjacent ANFIS models are trained on nearby regions with sufficient data, positioning the model for the sparse region in between. Experimental studies conducted on Martian image datasets under both sufficient and sparse data conditions validate the effectiveness of the proposed approach, in overcoming the specific challenges faced by the task of ISR in extraterrestrial imaging scenarios.