SupResDiffGAN a new approach for the Super-Resolution task

• URL: http://arxiv.org/abs/2504.13622v1
• Date: Fri, 18 Apr 2025 10:55:24 GMT
• Title: SupResDiffGAN a new approach for the Super-Resolution task
• Authors: Dawid Kopeć, Wojciech Kozłowski, Maciej Wizerkaniuk, Dawid Krutul, Jan Kocoń, Maciej Zięba,
• Abstract summary: SupResDiffGAN is a novel hybrid architecture that combines the strengths of Generative Adversarial Networks (GANs) and diffusion models for super-resolution tasks.<n>By leveraging latent space representations and reducing the number of diffusion steps, SupResDiffGAN achieves significantly faster inference times than other diffusion-based super-resolution models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we present SupResDiffGAN, a novel hybrid architecture that combines the strengths of Generative Adversarial Networks (GANs) and diffusion models for super-resolution tasks. By leveraging latent space representations and reducing the number of diffusion steps, SupResDiffGAN achieves significantly faster inference times than other diffusion-based super-resolution models while maintaining competitive perceptual quality. To prevent discriminator overfitting, we propose adaptive noise corruption, ensuring a stable balance between the generator and the discriminator during training. Extensive experiments on benchmark datasets show that our approach outperforms traditional diffusion models such as SR3 and I$^2$SB in efficiency and image quality. This work bridges the performance gap between diffusion- and GAN-based methods, laying the foundation for real-time applications of diffusion models in high-resolution image generation.

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