  Super-resolution (SR) is an ill-posed inverse problem with a large set of
feasible solutions that are consistent with a given low-resolution image.
Various deterministic algorithms aim to find a single solution that balances
fidelity and perceptual quality; however, this trade-off often causes visual
artifacts that bring ambiguity in information-centric applications. On the
other hand, diffusion models (DMs) excel in generating a diverse set of
feasible SR images that span the solution space. The challenge is then how to
determine the most likely solution among this set in a trustworthy manner. We
observe that quantitative measures, such as PSNR, LPIPS, DISTS, are not
reliable indicators to resolve ambiguous cases. To this effect, we propose
employing human feedback, where we ask human subjects to select a small number
of likely samples and we ensemble the averages of selected samples. This
strategy leverages the high-quality image generation capabilities of DMs, while
recognizing the importance of obtaining a single trustworthy solution,
especially in use cases, such as identification of specific digits or letters,
where generating multiple feasible solutions may not lead to a reliable
outcome. Experimental results demonstrate that our proposed strategy provides
more trustworthy solutions when compared to state-of-the art SR methods.
