How do you solve excessive noise in generated images when using a GAN for image-to-image translation

Question

With the help of programming, can you tell me how you solve excessive noise in generated images when using a GAN for image-to-image translation?

Answer 1

Excessive noise in generated images during image-to-image translation with GANs can be addressed by using techniques such as:

• Improved Loss Functions: Use perceptual loss, L1 loss, or feature matching loss to guide the generator towards more realistic outputs.
• Regularization: Apply techniques like spectral normalization to stabilize training and reduce noise.
• Discriminator Refinement: Improve the discriminator to better distinguish between real and generated images, helping the generator reduce noise.
• Noise Reduction Post-Processing: Use techniques like denoising autoencoders or filtering to clean up the generated images after generation.

Here is the code snippet you can refer to:

In the above code, we are using the following key points:

• L1 Loss: Helps reduce pixel-wise differences, leading to smoother outputs.
• Spectral Normalization: Controls the generator's capacity to reduce instability and noise.
• Post-Processing: Apply denoising techniques to clean the generated image.

Hence, these methods ensure that the generated images are sharper and more visually appealing by minimizing excessive noise.