Authors:
Gleb S. Brykin
and
Valeria Efimova
Affiliation:
ITMO University, Kronverksky Pr, St. Petersburg, Russia
Keyword(s):
Deep Learning, Image Super-Resolution, Image Restoration, Generative Artificial Intelligence, Generative-Adversarial Networks, Vision Transformer, Convolutional Neural Network.
Abstract:
Image super-resolution methods are increasingly divided into several groups, setting different goals for themselves, which leads to difficulties when using them in real conditions. While some methods maximize the accuracy of detail reconstruction and minimize the complexity of the model, losing realism, other methods use heavy architectures to achieve realistic images. In this paper, we propose a new class of image super-resolution methods called efficient real super-resolution, which occupies the gap between efficient and real super-resolution methods. The main goal of our work is to show the possibility of creating compact super-resolution models that allow generating realistic images, like SOTA in the field of real super-resolution, requiring only a few parameters and small computing resources. We compare our models with SOTA qualitatively and quantitatively using NIQE and LPIPS image naturalness metrics, getting unambiguous positive results. We also offer a self-contained cross-p
latform application that generates images comparable to SOTA in terms of realism in an acceptable time, and fits entirely on one 3.5-inch floppy disk.
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