Blurry outputs in GANs can be fixed by improving the discriminator's ability to distinguish fine details and enhancing the generator's capacity to produce sharper images. Here are the techniques you can follow:
- Use a Deeper Discriminator: A more complex discriminator can help identify finer details and promote sharper images.
- Add Perceptual Loss: Use a pre-trained network (e.g., VGG) to calculate perceptual loss, which focuses on high-level features rather than pixel-level similarity.
- Use a PatchGAN Discriminator: This technique applies a discriminator to image patches rather than the entire image, encouraging finer details.
- Use a High-Resolution Generator: Increase the generator's resolution, and/or use progressive growing for smoother, more detailed image generation.
Here is the code snippet you can refer to:
In the above code we are using the following key points:
- Perceptual Loss: Focuses on perceptual similarity using high-level features to improve image sharpness.
- Higher-Resolution Output: Use high-resolution generators and patch-based discriminators to encourage detail.
- Deeper Discriminators: Help the model focus on fine-grained features, reducing blurriness.
Hence, these methods can help the GAN generate sharper, more realistic images.
