Over the past few years deep learning-based techniques such as Generative Adversarial Networks (GANs) have significantly improved solutions to image super-resolution and image-to-image translation problems. In this paper, we propose a solution to the joint problem of image super-resolution and multi-modality image-to-image translation. The problem can be stated as the recovery of a high-resolution image in a modality, given a low-resolution observation of the same image in an alternative modality. Our paper offers two models to address this problem and will be evaluated on the recovery of high-resolution day images given low-resolution night images of the same scene. Promising qualitative and quantitative results will be presented for each model.
@article{arxiv.2206.09193,
title = {Multi-Modality Image Super-Resolution using Generative Adversarial Networks},
author = {Aref Abedjooy and Mehran Ebrahimi},
journal= {arXiv preprint arXiv:2206.09193},
year = {2022}
}
Comments
to be published in the Proceedings of 16th International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing (CGVCVIP), Lisbon, Portugal, July 2022