RUNet: A Robust UNet Architecture for Image Super-Resolution.

Xiaodan Hu, Mohamed A. Naiel, Alexander Wong, Mark Lamm, Paul W. Fieguth

Abstract: Single image super-resolution (SISR) is a challenging ill-posed problem which aims to restore or infer a high-resolution image from a low-resolution one. Powerful deep learning-based techniques have achieved state-of-the-art performance in SISR; however, they can underperform when handling images with non-stationary degradations, such as for the application of projector resolution enhancement. In this paper, a new UNet architecture that is able to learn the relationship between a set of degraded low-resolution images and their corresponding original high-resolution images is proposed. We propose employing a degradation model on training images in a non-stationary way, allowing the construction of a robust UNet (RUNet) for image super-resolution (SR). Experimental results show that the proposed RUNet improves the visual quality of the obtained super-resolution images while maintaining a low reconstruction error.