How good is your Super-Resolution Image? Quality assurance in image reconstruction using the bootstrap

Super-Resolution Image Reconstruction is known to be sensitive to errors in assumptions such as accurate sub-pixel motion estimation. Even small errors can yield a significant degradation of image quality that complicates any follow-on task such as object detection or classification. We focus on the problem of automatic quality assessment of Super-Resolution image reconstruction. We propose a bootstrap-based method that provides an objective metric quantifying reconstruction quality and thus allowing to readjust the reconstruction.