HDW-SR: High-Frequency Guided Diffusion Model based on Wavelet Decomposition for Image Super-Resolution

TL;DR

HDW-SR introduces a wavelet-based high-frequency guided diffusion approach for image super-resolution, focusing on residual maps and multi-scale frequency decomposition to enhance fine detail recovery.

Contribution

It proposes a novel wavelet decomposition-based diffusion framework with high-frequency guidance and a dynamic thresholding block for improved image super-resolution.

Findings

Achieves competitive super-resolution performance on synthetic and real datasets.

Excels in recovering fine-grained image details.

Utilizes wavelet-based multi-scale frequency decomposition for explicit high-frequency guidance.

Abstract

Diffusion-based methods have shown great promise in single image super-resolution (SISR); however, existing approaches often produce blurred fine details due to insufficient guidance in the high-frequency domain. To address this issue, we propose a High-Frequency Guided Diffusion Network based on Wavelet Decomposition (HDW-SR), which replaces the conventional U-Net backbone in diffusion frameworks. Specifically, we perform diffusion only on the residual map, allowing the network to focus more effectively on high-frequency information restoration. We then introduce wavelet-based downsampling in place of standard CNN downsampling to achieve multi-scale frequency decomposition, enabling sparse cross-attention between the high-frequency subbands of the pre-super-resolved image and the low-frequency subbands of the diffused image for explicit high-frequency guidance. Moreover, a Dynamic Thresholding Block (DTB) is designed to refine high-frequency selection during the sparse attention process. During upsampling, the invertibility of the wavelet transform ensures low-loss feature reconstruction. Experiments on both synthetic and real-world datasets demonstrate that HDW-SR achieves competitive super-resolution performance, excelling particularly in recovering fine-grained image details. The code will be available after acceptance.