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

• URL: http://arxiv.org/abs/2511.13175v1
• Date: Mon, 17 Nov 2025 09:25:26 GMT
• Title: HDW-SR: High-Frequency Guided Diffusion Model based on Wavelet Decomposition for Image Super-Resolution
• Authors: Chao Yang, Boqian Zhang, Jinghao Xu, Guang Jiang,
• Abstract summary: We propose a High-Frequency Guided Diffusion Network based on Wavelet Decomposition (HDW-SR)<n>We perform diffusion only on the residual map, allowing the network to focus more effectively on high-frequency information restoration.<n> Experiments on both synthetic and real-world datasets demonstrate that HDW-SR achieves competitive super-resolution performance.
• Score: 4.388490927225987
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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