In recent years, diffusion models have emerged as a superior alternative to generative adversarial networks (GANs) for high-fidelity image generation, with wide applications in text-to-image generation, image-to-image translation, and super-resolution. However, their real-time feasibility is hindered by slow training and inference speeds. This study addresses this challenge by proposing a wavelet-based conditional Diffusion GAN scheme for Single-Image Super-Resolution (SISR). Our approach utilizes the diffusion GAN paradigm to reduce the timesteps required by the reverse diffusion process and the Discrete Wavelet Transform (DWT) to achieve dimensionality reduction, decreasing training and inference times significantly. The results of an experimental validation on the CelebA-HQ dataset confirm the effectiveness of our proposed scheme. Our approach outperforms other state-of-the-art methodologies successfully ensuring high-fidelity output while overcoming inherent drawbacks associated with diffusion models in time-sensitive applications.

本研究解决了扩散模型在高保真图像生成中的实时可行性问题，提出了一种基于小波的条件扩散生成对抗网络方案，专注于单幅图像超分辨率。通过减少反向扩散过程所需的时间步和利用离散小波变换，显著降低了训练和推理时间，实验结果表明该方案在CelebA-HQ数据集上优于其他最先进的方法，确保了高保真输出。

基于小波扩散生成对抗网络的图像超分辨率