Deep learning based image Super-resolution for nonlinear lens distortions

Recent developments of virtual reality applications have accelerated the usage of cameras with wide-angle and telephoto/macro lens, which produce nonlinear radial lens distortions, such as barrel distortion and pincushion distortion. However, due to many reasons, the resolution of images with nonlinear lens distortions is often limited. In this paper, we address the image super-resolution (SR) for images with nonlinear lens distortions through the deep convolutional neutral network with residual learning, which can significantly improve the image quality before and after the camera calibration. The proposed deep learning network was trained using hundreds of simulated images and tested on real cameras with fisheye and macro lens. Experimental results show that the proposed image SR method outperforms state-of-the-art SR methods for various degrees of radial-based barrel and pincushion distortions.