Implemented Photo-Realistic Image Super-Resolution using SRGAN

Implemented a Super-Resolution Generative Adversarial Network (SRGAN) in Python to upscale low-resolution images by 4x, utilizing perceptual loss functions (content loss from VGG feature maps combined with adversarial loss) to generate realistic high-frequency textures.

Demonstrated the advantages of adversarial training over traditional interpolation algorithms.

Minimizing Mean Squared Error (MSE) produces overly smooth, blurry outputs because it averages potential high-frequency solutions. Using perceptual loss coupled with an adversarial discriminator forces the generator to synthesize sharp, plausible high-frequency details.

Incorporate modern ESRGAN improvements or residual-in-residual dense blocks (RRDB) to eliminate minor checkerboard artifacts in high-contrast regions.