High-Resolution Image Enhancement

Abstract

High-resolution image enhancement is achieved by applying two advanced super-resolution techniques: Fast Super-Resolution Convolutional Neural Networks (FSRCNN) and Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN). FSRCNN enables efficient upscaling through a lightweight deep learning architecture, ensuring real-time processing while preserving structural details. In parallel, ESRGAN leverages adversarial training and perceptual loss to generate sharper, more visually realistic images, making it ideal for applications demanding high visual fidelity. The system accepts a single low-resolution image as input and produces two distinct highresolution outputs—each corresponding to the result of FSRCNN and ESRGAN processing. A user-friendly web interface supports easy image uploads, model selection, and real-time enhancement tracking. The application is built using a modular architecture that separates presentation, logic, and data layers, ensuring maintainability and future scalability. Performance evaluation using quantitative metrics such as PSNR and SSIM, along with qualitative user feedback, demonstrates that each model contributes unique strengths in resolution enhancement. This dual-model approach not only improves the accuracy and flexibility of image enhancement but also highlights the effectiveness of combining different deep learning methodologies for practical and diverse use cases. The project contributes to the advancement of AI-driven super-resolution and lays the groundwork for future exploration of hybrid techniques in image processing and computer vision.