How to Implement a Super-Resolution Generative Adversarial Network (SRGAN)
3 min readFeb 19, 2024
A Super-Resolution Generative Adversarial Network (SRGAN) is a powerful model in the field of image processing, capable of creating high-resolution (HR) images from low-resolution (LR) ones. Think of it like taking a blurry picture and turning it into a sharp, detailed one. Here’s what you need to know about SRGANs:
What they do:
- Take a low-resolution image as input.
- Use deep learning techniques to “upscale” the image, meaning they increase the number of pixels while preserving details and textures.
- Aim to create photo-realistic HR images, not just blurry enlargements.
How they work:
- SRGANs are a type of Generative Adversarial Network (GAN), which involve two competing neural networks:
- Generator: Creates the HR image from the LR input.
- Discriminator: Tries to distinguish real HR images from the ones generated by the generator.
- Through this competition, the generator learns to create increasingly realistic HR images that fool the discriminator.
- SRGANs also use a perceptual loss function, which compares the generated image to real HR images based on how they are perceived by humans, not just…