WGID: wavelet-guided iterative detail enhancement diffusion models for zero-shot image super-resolution
Junjie Lu, Hongyi Liu, Zhihui Wei · 2025
Zero-shot image super-resolution methods have attracted considerable attention due to their high potential for applications with few additional training data. However, these methods often encounter challenges such as inconsistent results and blurred details. To mitigate these issues, we propose WGID: Wavelet-Guided Iterative Detail Enhancement Diffusion Models for single-image super-resolution. In this method, diffusion iteration is guided by wavelet transform to enhance details across different scales while maintaining the similarity between the reference and diffusion-generated images. In addition, the reference images are dynamically updated to provide a suitable guidance during the diffusion process. Meanwhile, the diffusion model progressively refines details, suppresses noise and preserves the natural appearance of the generated image. By integrating these two techniques, the proposed method produces reconstructed super-resolution image with enhanced visual quality, clear details, and more realistic results, accompanied by improved assessment metrics.