Marco Rendina
Sessions
In recent years we have seen a surge of interest in the Cultural Heritage (CH) sector towards exploring and adopting AI. First making waves in other professional sectors, AI solutions have come around to show promising results in different areas of operations of Cultural Heritage Institutions - from content analysis and knowledge extraction to machine translation and enrichment of metadata. However, a number of technical and knowledge barriers that limit the broader uptake of AI in the sector persist. At the same time, ICT actors encounter a number of challenges when it comes to efficiently transferring AI techniques that have been successful in other sectors to the CH domain. In this panel we invite developers, brokers, promoters and experts of AI to share their insights into the opportunities waiting to be seized and the challenges faced by both CH and technology stakeholders. Will our assumptions be confirmed or debunked?
Many images on Europeana.eu are in low resolution and don't meet user expectations or the requirements set by the Europeana Publishing Framework. Images often suffer from degradations including camera blur, sensor noise, sharpening artifacts, and JPEG compression.
Image Super-Resolution (SR) techniques aim to enhance the visual quality and refine the details of low-resolution images by generating corresponding high-resolution versions with finer details. Recent advances in SR rely heavily on deep learning models that leverage data-driven approaches to accurately reconstruct missing details. However, assessing the quality of the SR output is challenging, as commonly used quantitative metrics like PSNR and SSIM only loosely correlate with human perception.
Different deep learning models may perform better on specific types of images, such as photos, drawings, and prints. Following the experiments of the Europeana Foundation R&D team, at EFHA, in order to determine the most suitable SR model for different image types, we developed a framework for evaluating various deep learning models using selected images from the EFHA collections on Europeana.eu. Through a crowdsourcing visual comparison tool, users have the ability to browse the EFHA test collection, zoom in on different areas of the images and compare and rank the results obtained from different SR models.
By leveraging user feedback and incorporating visual comparison tools, our framework aims to improve the quality of images within Europeana.eu and provide insights into selecting optimal SR models based on image type. Join this session to find out more.