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A promising direction for recovering the lost information in low-resolutionheadshot images is utilizing a set of high-resolution exemplars from the sameidentity. Complementary images in the reference set can improve the generatedheadshot quality across many different views and poses. However, it ischallenging to make the best use of multiple exemplars: the quality andalignment of each exemplar cannot be guaranteed. Using low-quality andmismatched images as references will impair the output results. To overcomethese issues, we propose an efficient Headshot Image Super-Resolution withMultiple Exemplars network (HIME) method. Compared with previous methods, ournetwork can effectively handle the misalignment between the input and thereference without requiring facial priors and learn the aggregated referenceset representation in an end-to-end manner. Furthermore, to reconstruct moredetailed facial features, we propose a correlation loss that provides a richrepresentation of the local texture in a controllable spatial range.Experimental results demonstrate that the proposed framework not only hassignificantly fewer computation cost than recent exemplar-guided methods butalso achieves better qualitative and quantitative performance.

HIME: Efficient Headshot Image Super-Resolution with Multiple Exemplars