High-Resolution Image Generation Using Artificial Intelligence and Diffusion Modelling

Super-Resolution via Repeated Refinement (SR3) is a state-of-the-art super-resolution algorithm based on diffusion model that can enhance the resolution of images. This method is used to pre-trained models on large datasets and can be used for various tasks without requiring training from scratch. Training SR3 from scratch using the ImageNet dataset involves a complex process that requires substantial computational resources and expertise. The idea is applied the trained SR3 model to new images by feeding the low-resolution inputs and obtaining the high-resolution outputs. It's important to note that training SR3 from scratch is a resource-intensive process that requires powerful GPUs and significant computation time. If you do not have access to such resources, an alternative is to use pre-trained models that are already available and fine-tune them on specific datasets or tasks. The paper shows the result of comparing the resolution of the preprocessed images using a significantly smaller number of images to perform the training with those obtained using the pre-trained model. The results obtained show acceptable results without having to perform on large datasets minimizing the computation time to obtain the resolution of images.
This work is partially supported by the Spanish Ministry of Science and Innovation under contract PID2021-124463OB-100 and PID2019-107255GB-C22 by the Generalitat de Catalunya under grant 2021-SGR-00326 and 2021-SGR-01007. Finally, the research leading to these results also has received funding from the European Union's Horizon 2020 research and innovation program under the HORIZON-EU VITAMIN-V (101093062) project.