GAN-based Super Resolution is a fascinating field in AI that focuses on enhancing the quality of images. This tutorial will guide you through the basics of GANs and their application in super-resolution.

What is GAN-based Super Resolution?

GAN stands for Generative Adversarial Network. It consists of two neural networks: a generator and a discriminator. The generator tries to create realistic images, while the discriminator tries to distinguish between real images and generated ones. This competition helps the generator improve over time.

Key Concepts

  • Generator: This network generates high-resolution images from low-resolution inputs.
  • Discriminator: This network evaluates the quality of the generated images.
  • Loss Function: The loss function measures the difference between the generated images and the ground truth.

Steps to Implement GAN-based Super Resolution

  1. Data Preparation: Collect a dataset of low-resolution and high-resolution images.
  2. Model Architecture: Design the generator and discriminator networks.
  3. Training: Train the generator and discriminator together to improve the quality of the generated images.
  4. Evaluation: Evaluate the performance of the model using metrics like PSNR (Peak Signal-to-Noise Ratio).

Example Code

Further Reading

For more in-depth information, check out our Deep Learning Tutorial.

Images

Here are some examples of images that can be used for GAN-based Super Resolution:

Golden_Retriever
Labrador_Retriever
Bulldog