Enhancing Image Resolution using Hybrid model(Enhanced Super Resolution Generative Adversarial Network)

In this era characterized by high demand for quality images, it is apparent the resolution of any picture matters a lot in different fields. The image enhancement procedures available in the market do not usually address the issue of clarity or improving any fine detail of the picture and therefore advanced methods are being sought. Thus, this paper investigates the use of a hybrid deep learning model called Enhanced Super Resolution Generative Adversarial Network (ESRGAN) to upscale images in terms of resolution. Utilizing the Generative Adversarial Networks (GAN) more effectively along with other superior levels of deep learning, ESRGAN has brought a dramatic improvement in the quality of images without losing important details. Both qualitative and quantitative approaches have been employed in this research to compare the performance of ESRGAN with other conventional methods and assert its effectiveness in enhancing image resolution. Despite its strengths, limitations remain, particularly the model's ability to upscale resolution by only 50% and its reliance on high-performance GPU hardware for optimal results. The study highlights ESRGAN's transformative impact on image processing, setting a foundation for future innovations and applications across industries.