SRZoo: An integrated repository for super-resolution using deep learning
This provides a centralized resource for researchers and practitioners in image processing, though it is incremental as it organizes existing methods rather than introducing new algorithms.
The authors tackled the problem of dispersed implementations and evaluations of deep learning-based super-resolution methods by creating SRZoo, an integrated repository that provides state-of-the-art models, conversion toolkits, and evaluation tools in a single place, with the software and models publicly available on GitHub.
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.