Anomaly Guided Generalizable Super-Resolution of Chest X-Ray Images Using Multi-level Information Rendering

Single image super-resolution (SISR) methods aim to generate a high-resolution image from the corresponding low-resolution images. Such methods may be useful in improving the resolution of medical images including chest x-rays. Medical images with superior resolution may subsequently lead to an improved diagnosis. However, SISR methods for medical images are relatively rare. We propose a SISR method for chest x-ray images. Our method uses multi-level information rendering by utilizing the cue about the abnormality present in the images. Experiments on publicly available datasets show the superiority of the proposed method over several state-of-the-art approaches.