A Wavelet Diffusion GAN for Image Super-Resolution (2410.17966v1)

Abstract: 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.

• The paper proposes a hybrid approach that integrates Discrete Wavelet Transform with diffusion GANs to significantly reduce the number of required diffusion steps.
• It achieves superior image quality and faster processing by leveraging metrics like PSNR, SSIM, LPIPS, and FID, outperforming models on the CelebA-HQ dataset.
• The method paves the way for efficient, real-time super-resolution applications by addressing key limitations in traditional pixel-space diffusion models.

WaDiGAN-SR: A Wavelet-based Diffusion GAN Approach to Image Super-Resolution

The paper "WaDiGAN-SR: A Wavelet-based Diffusion GAN Approach to Image Super-Resolution" presents a novel methodology that enhances the capabilities of image super-resolution (ISR) by integrating wavelet theory into diffusion and generative adversarial networks (GANs). This approach addresses the limitations encountered in current diffusion models related to pixel-space diffusion, introducing an innovative solution that leverages both the Discrete Wavelet Transform (DWT) and the computational efficiency of Diffusion GANs.

Overview

The research outlines the substantial improvements diffusion models offer in high-fidelity image generation, marked by stability and flexibility over traditional GANs. Despite these advancements, pixel-space diffusion models remain constrained by slow training and inference speeds, which are impractical for real-time applications. Latent space diffusion attempts to ameliorate these constraints but typically requires large amounts of data for training a variability autoencoder (VAE).

Methodology

The main contribution of WaDiGAN-SR lies in a strategic hybridization of wavelet-based processes with Diffusion GANs to expedite the diffusion process. By applying the Discrete Wavelet Transform to images and feature layers, WaDiGAN-SR decomposes inputs into low and high-frequency sub-bands, thereby facilitating spatial dimension reduction and enhancing image detail. This technique significantly reduces the number of steps required in the reverse diffusion process compared to conventional diffusion methods.

Results

The authors validate their model using the CelebA-HQ dataset, comparing it against established ISR models like ESRGAN, SR3, and DiWa. Performance is assessed using robust metrics such as PSNR, SSIM, LPIPS, and FID. The findings demonstrate that WaDiGAN-SR surpasses these baselines in both speed and image quality. Notably, the integration of wavelet sub-bands contributes to a significant reduction in training and inference durations while maintaining high output fidelity.

Implications and Future Work

WaDiGAN-SR signifies a leap towards achieving efficient and real-time applicability of diffusion models in ISR, addressing critical bottlenecks in both speed and data processing. The approach's reliance on wavelet-based techniques for spatial reduction and detail enhancement presents a promising avenue for further research. Future explorations might extend this methodology to additional applications in computer vision where high-quality and swift image processing is essential.

In conclusion, this paper delineates a noteworthy advancement in ISR techniques by introducing wavelet-based diffusion GANs, marking a pivotal development toward more practical deployment of diffusion models in real-world scenarios.