MDA-SR: Multi-level Domain Adaptation Super-Resolution for Wireless Capsule Endoscopy Images

Super-resolution (SR) of wireless capsule endoscopy (WCE) images is challenging because paired high-resolution (HR) images are not available. An intuitive solution is to simulate paired low-resolution (LR) WCE images from HR electronic endoscopy images for supervised learning. However, the SR model obtained by this method cannot be well adapted to real WCE images due to the large domain gap between electronic endoscopy images and WCE images. To address this issue, we propose a Multi-level Domain Adaptation SR model (MDA-SR) in an unsupervised manner using arbitrary set of WCE images and HR electronic endoscopy images. Our approach implements domain adaptation at the image level and latent level during the degradation and SR processes, respectively. To the best of our knowledge, this is the first work to explore an unsupervised SR approach for WCE images. Furthermore, we design an Endoscopy Image Quality Evaluator (EIQE) based on the reference-free image evaluation metric NIQE, which is more suitable for evaluating WCE image quality. Extensive experiments demonstrate that our MDA-SR method outperforms state-of-the-art SR methods both quantitatively and qualitatively.