Srzoo: An Integrated Repository For Super-Resolution Using Deep Learning

Abstract

Deep learning-based image processing algorithms, including image super-resolution methods, have been proposed with significant improvement in performance in recent years. However, their implementations and evaluations are dispersed in terms of various deep learning frameworks and various evaluation criteria. In this paper, we propose an integrated repository for the super-resolution tasks, named SRZoo, to provide state-of-the-art super-resolution models in a single place. Our repository offers not only converted versions of existing pre-trained models, but also documentation and toolkits for converting other models. In addition, SRZoo provides platform-agnostic image reconstruction tools to obtain super-resolved images and evaluate the performance in place. It also brings the opportunity of extension to advanced image-based researches and other image processing models. The software, documentation, and pre-trained models are publicly available on GitHub 1 .

Front-end deep learning web apps development and deployment: a review

“…22 Two repositories that are not connected to any specific framework are Model Zoo 23 and ModelHub. 24 Model Zoo curates and hosts a repository of open-source deep learning code and pre-trained models for various platforms and applications. The repositories also offer filtering capabilities to assist users in locating the models they need.…”

“…• SRZoo 25 in [24] is a centralized repository for superresolution tasks that collects super-resolution models in TensorFlow. • ARBML 26 in [6] showcased a series of TensorFlow.js NLP models trained for Arabic, which support the NLP pipeline development.…”