  In this study, we evaluate the performance of multiple state-of-the-art SRGAN
(Super Resolution Generative Adversarial Network) models, ESRGAN, Real-ESRGAN
and EDSR, on a benchmark dataset of real-world images which undergo degradation
using a pipeline. Our results show that some models seem to significantly
increase the resolution of the input images while preserving their visual
quality, this is assessed using Tesseract OCR engine. We observe that EDSR-BASE
model from huggingface outperforms the remaining candidate models in terms of
both quantitative metrics and subjective visual quality assessments with least
compute overhead. Specifically, EDSR generates images with higher peak
signal-to-noise ratio (PSNR) and structural similarity index (SSIM) values and
are seen to return high quality OCR results with Tesseract OCR engine. These
findings suggest that EDSR is a robust and effective approach for single-image
super-resolution and may be particularly well-suited for applications where
high-quality visual fidelity is critical and optimized compute.
