Keypoint-based metric for evaluating image super-resolution quality

Recent advances in single- and multi-image super-resolution have revealed the limitations of classical image similarity metrics (like peak signal-to-noise ratio), as they often fail to align with human perception when evaluating the visual quality of super-resolved outputs. In this paper, we explore how to exploit keypoint-based metrics to evaluate super-resolution image quality. Specifically, we explore two correlated metrics: (i) a multiscale index proposal measure capturing salience of keypoints, and (ii) a repeatability metric quantifying how consistently the corresponding keypoints are identified in super-resolved and ground-truth images. Experiments on several simulated and real-world datasets show that the repeatability correlates with subjective judgments, and multi-scale index proposal can be helpful for difficult datasets when other metrics are insufficient.