IMAGE SUPER RESOLUTION USING MACHINE LEARNING

ABSTRACT This research presents the development and evaluation of an Image Super-Resolution (ISR) system using a deep learning approach. The study focuses on improving the quality of low-resolution images by leveraging the capabilities of the Super-Resolution Convolutional Neural Network (SRCNN). The system was trained and validated on a dataset of diverse images, allowing it to learn detailed patterns and reconstruct high-resolution outputs effectively. The evaluation metrics, including loss and mean squared error (MSE), demonstrate that the model successfully enhances image clarity while preserving important structural features. The experimental results show that the developed SRCNN model consistently improves image quality across multiple test cases. Test cases involving uploaded image files confirmed that the system could generate high-quality outputs with reduced noise and better edge sharpness. The training process, consisting of 50 epochs, showed steady improvements in validation loss, indicating the model’s ability to generalize effectively to unseen data. Visual comparisons between original and enhanced images validate the model’s practical utility in ISR applications. The study concludes that the proposed ISR system offers a reliable and efficient solution for image enhancement tasks. It highlights the potential of deep learning-based ISR methods in various applications, including medical imaging, satellite imagery, and multimedia content enhancement. Future work can focus on integrating advanced architectures, real-time processing, and larger datasets to further optimize performance and applicability. This research contributes to the growing field of image restoration and enhancement, providing a foundation for both academic and industrial applications.