Image Super Resolution using U-Net architecture and SRGAN: Comparative Analysis

Super-resolution is an important image processing task that improves the resolution of high-resolution images. Low resolution is used in many different fields, including medical imaging, satellite imagery, and computer vision. This paper presents a new approach to ultra-high resolution using the U-Net architecture, a deep learning framework known for its success in image segmentation and restoration tasks.In this study, we propose an adaptive U-Net model specifically designed for ultra-high-resolution tasks. The architecture includes an encoder-decoder network with bypass connections, enabling multi-scale feature extraction and high-resolution detail reconstruction. Our model is trained on a diverse dataset of low- and high-resolution image pairs, allowing it to learn complex relationships and patterns in images.We evaluate the performance of the U-Net based superresolution method using standard image quality metrics and qualitative visual evaluation. Test results show a significant improvement in image quality, with improved sharpness, texture, and detail recovery. Furthermore, our model outperforms state-of-the-art super-resolution methods in terms of signal-to-noise ratio (PSNR) and structural similarity index (SSIM).