Capturing Ground Truth Super-Resolution Data

Super-resolution (SR) offers an effective approach to boost quality and details of low-resolution (LR) images to obtain high-resolution (HR) images. Despite the theoretical and technical advances in the past decades, it still lacks plausible methodology to evaluate and compare different SR algorithms. The main cause to this problem lies in the missing ground truth data for SR. Unlike in many other computer vision tasks, where existing image datasets can be utilized directly, or with a little extra annotation work, evaluating SR requires that the dataset contain both LR and the corresponding HR ground truth images of the same scene captured at the same time.This work presents a novel prototype camera system to address the aforementioned difficulties of acquiring ground truth SR data. Two identical camera sensors equipped with a wide-angle lens and a telephoto lens respectively, share the same optical axis by placing a beam splitter in the optical path. The back-end program can then trigger their shutters simultaneously and precisely register the region of interests (ROIs) of the LR and HR image pairs in an automated manner free of sub-pixel interpolation. Evaluation results demonstrate the special characteristics of the captured ground truth HR LR face images compared to the simulated ones. The dataset is made freely available for noncommercial research purposes.