This project focuses on the restoration of hyperspectral images using Classical Generative Adversarial Networks (GANs), specifically employing the Super-Resolution Generative Adversarial Network (SRGAN) model to improve image resolution. Hyperspectral imaging, which captures a wide range of wavelengths across the electromagnetic spectrum, provides invaluable data for various fields such as remote sensing, environmental monitoring, and medical imaging. However, the inherent challenge of low-resolution images hinders the extraction of precise features. To address this, we utilize the SRGAN model, a deep learning-based approach, to upscale low-resolution hyperspectral images into higher resolution ones, enhancing their quality and interpretability. The SRGAN framework leverages the power of adversarial training to generate realistic high-resolution images by minimizing perceptual loss, ensuring that restored images not only have high pixel fidelity but also contain crucial spectral details. The project also aims to develop a scalable web application that incorporates this model, making it accessible and user-friendly for researchers and professionals to enhance hyperspectral image quality on-demand. This approach promises to facilitate more accurate analysis and interpretation of hyperspectral data, contributing to advancements in various scientific and industrial applications.

Keywords: Hyperspectral imaging, Generative Adversarial Networks, SRGAN, Image restoration, Super-Resolution, Deep learning, High-resolution images, Perceptual loss, Web application, Remote sensing, Image enhancement, Spectral data.