Lornatang/SRGAN-PyTorch

What it solves

This project addresses the problem of recovering fine texture details when upscaling images by large factors. Traditional super-resolution methods often produce images that lack high-frequency details and look perceptually unsatisfying because they focus on minimizing pixel-level reconstruction error.

How it works

It is a PyTorch reimplementation of the SRGAN (Super-Resolution Generative Adversarial Network) architecture. The system uses a generative adversarial network consisting of a deep residual network (the generator) and a discriminator. It employs a perceptual loss function combining adversarial loss—which pushes the output toward the manifold of natural images—and content loss, which focuses on perceptual similarity rather than simple pixel-space similarity.

Who it’s for

This is designed for researchers and developers interested in image super-resolution, generative adversarial networks, and those looking for a faithful PyTorch implementation of the original SRGAN paper.

Highlights

• Op-for-op PyTorch reimplementation of the SRGAN paper.
• Supports 4x upscaling factors for photo-realistic results.
• Includes scripts for downloading weights and datasets (ImageNet, Set5).
• Provides both SRResNet and SRGAN model training and testing configurations.