Evaluating Super-Resolution of Satellite Images: A Proba-V Case Study

Super-resolution reconstruction is a process aimed at enhancing image spatial resolution. To evaluate the quality of super-resolution, the reconstruction outcome is compared with a ground-truth reference image, and the dissimilarity between them is commonly treated as a determinant of the reconstruction quality. While this is straightforward for simulated data, it becomes more challenging for real-world scenarios, in which reference images and the reconstruction inputs are acquired using different imaging sensors. In such cases, the dissimilarity also results from other factors concerned with different sensor characteristics. In a recently organized Proba-V Super Resolution Challenge, the reconstruction quality was assessed using a modified peak signal-to-noise ratio which compensates for small shifts and global changes in the brightness. In the study reported here, we investigate a number of image similarity metrics to verify their robustness against different levels of distortions applied to Proba-V images. We expect that the reported results will help in choosing appropriate metrics while developing new super-resolution solutions aimed at real-world scenarios.