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The PROBA-V Super-Resolution challenge distributes real low-resolution imageseries and corresponding high-resolution targets to advance research onMulti-Image Super Resolution (MISR) for satellite images. However, in thePROBA-V dataset the low-resolution image corresponding to the high-resolutiontarget is not identified. We argue that in doing so, the challenge ranks theproposed methods not only by their MISR performance, but mainly by theheuristics used to guess which image in the series is the most similar to thehigh-resolution target. We demonstrate this by improving the performanceobtained by the two winners of the challenge only by using a differentreference image, which we compute following a simple heuristic. Based on this,we propose PROBA-V-REF a variant of the PROBA-V dataset, in which the referenceimage in the low-resolution series is provided, and show that the rankingbetween the methods changes in this setting. This is relevant to many practicaluse cases of MISR where the goal is to super-resolve a specific image of theseries, i.e. the reference is known. The proposed PROBA-V-REF should betterreflect the performance of the different methods for this reference-aware MISRproblem.

Proba-V-ref: Repurposing the Proba-V challenge for reference-aware super resolution