Abstract
Deep learning has made significant advances in a variety of domains in recent years, including recognition of images, speech, and natural language processing and video super-resolution. Recovery of a high-resolution (HR) picture from a low-resolution (LR) counterpart is the aim of super-resolution (SR). It is an enduring and difficult part of image processing with numerous real-world applications including reconstruction of medical images, face recognition, HDTV, UAV surveillance, super-resolution panoramic video, and remote sensing. We aim to develop an application to provide a one-stop solution for converting both low-resolution images and videos to high-resolution images and videos. Our intention is to deliver the power of AI and deep learning to the general community by wrap** it in an API that can be seamlessly integrated with a Web application so that users can experience the benefits of the method of super-resolution using deep learning without reinventing the wheel.
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Keshari, S. et al. (2024). Visual Media Super-Resolution Using Super-Resolution Generative Adversarial Networks. In: Choudhury, T., Koley, B., Nath, A., Um, JS., Patidar, A.K. (eds) Geo-Environmental Hazards using AI-enabled Geospatial Techniques and Earth Observation Systems. Advances in Geographic Information Science. Springer, Cham. https://doi.org/10.1007/978-3-031-53763-9_17
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