SR-FDN: A Frequency-Domain Diffusion Network for Image Detail Restoration in Super-Resolution

LI Xiumei; DING Linlin; SUN Junmei; BAI Huang

doi:10.11999/JEIT250224

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LI Xiumei, DING Linlin, SUN Junmei, BAI Huang. SR-FDN: A Frequency-Domain Diffusion Network for Image Detail Restoration in Super-Resolution[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250224

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LI Xiumei, DING Linlin, SUN Junmei, BAI Huang. SR-FDN: A Frequency-Domain Diffusion Network for Image Detail Restoration in Super-Resolution[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250224

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SR-FDN: A Frequency-Domain Diffusion Network for Image Detail Restoration in Super-Resolution

doi: 10.11999/JEIT250224 cstr: 32379.14.JEIT250224

School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China

Funds: The China-Croatia Bilateral Science & Technology Cooperation Project

Received Date: 2025-04-01
Rev Recd Date: 2025-07-23

Available Online: 2025-08-05

Abstract

Abstract

Objective Image Super-Resolution (SR) is a critical computer vision task aimed at reconstructing High-Resolution (HR) images from Low-Resolution (LR) inputs, with broad applications in fields such as medical imaging and satellite imaging. Recently, diffusion-based SR methods have attracted significant attention due to their generative capability and strong performance in restoring fine image details. Existing diffusion model-based SR approaches have demonstrated potential in recovering textures and structures, with some methods focusing on spatial domain features and others utilizing frequency domain information. Spatial domain features aid in reconstructing overall structural information, whereas frequency domain decomposition separates images into amplitude and phase components across frequencies. High-frequency components capture details, textures, and edges, whereas low-frequency components describe smooth structures. Compared to purely spatial modeling, frequency domain features improve the aggregation of dispersed high-frequency information, enhancing the representation of image textures and details. However, current frequency domain SR methods still show limitations in restoring high-frequency details, with blurring or distortion persisting in some scenarios. To address these challenges, this study proposes SR-FDN, a SR reconstruction network based on a frequency-domain diffusion model. Methods SR-FDN leverages the distribution mapping capability of diffusion models to improve image reconstruction. The proposed network integrates spatial and frequency domain features to enhance high-frequency detail restoration. Two constraints guide the model design: (1) The network must generate plausible HR images conditioned solely on LR inputs, which serve as the primary source of structural information, ensuring high-fidelity reconstruction. (2) The model should balance structural reconstruction with enhanced detail restoration. To achieve this, a dual-branch frequency domain attention mechanism is introduced. A portion of the features undergoes Fourier transform for frequency domain processing, where high-frequency information is emphasized through self-attention. The remaining features adjust frequency domain weights before being combined with spatial domain representations. Skip connections in the U-Net architecture preserve LR structural information while enhancing frequency domain details, improving both structural and textural reconstruction. Wavelet downsampling replaces conventional convolutional downsampling within the U-Net noise predictor, reducing spatial resolution while retaining more detailed information. In addition, a Fourier frequency domain loss function constrains amplitude and phase components of the reconstructed image, further enhancing high-frequency detail recovery. To guide the generative process, additional image priors are incorporated, enabling the diffusion model to restore textures consistent with semantic category features. Results and Discussions The results of SR-FDN on face datasets and general datasets for 4× and 8× SR (Table 1, Table 2, Table 3) demonstrate that the proposed method achieves strong performance across objective evaluation metrics. These results indicate that SR-FDN can effectively restore image detail information while better preserving structural and textural features. A comparison of iteration counts between SR-FDN and two diffusion-based methods (Fig. 2) shows that SR-FDN can reconstruct higher-quality images with fewer iterations. Despite the reduced number of iterations, SR-FDN maintains high-fidelity reconstruction, reflecting its ability to lower computational overhead without compromising image quality. To further verify the effectiveness of the proposed SR-FDN, visual comparisons on the FFHQ dataset (Fig. 3) and the DIV2K dataset (Fig. 4) are presented. The results show that SR-FDN offers clearer and more detailed image reconstruction, particularly in high-frequency regions such as facial features and hair textures. Ablation experiments (Table 5) and feature visualization results (Fig. 5) are also provided. These results confirm that the proposed dual-branch frequency domain design and the Fourier domain loss function significantly contribute to improved restoration of fine details. Conclusions This study proposes SR-FDN, a diffusion-based SR reconstruction model that integrates frequency domain information to enhance detail restoration. The SR-FDN model incorporates a dual-branch frequency domain attention mechanism, which adaptively reinforces high-frequency components, effectively addressing the limitations of conventional methods in recovering edge structures and texture details. In addition, SR-FDN employs wavelet downsampling to preserve informative features while reducing spatial resolution, and introduces a frequency domain loss function that constrains amplitude and phase information, enabling more effective fusion of frequency and spatial domain features. This design substantially enhances the model’s ability to recover high-frequency details. Extensive experiments on benchmark datasets demonstrate that SR-FDN reconstructs images with superior quality and richer details, exhibiting clear advantages in both qualitative and quantitative evaluations.
- Deep learning,
- Single image super-resolution,
- Diffusion model,
- Frequency domain transform

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References(24)

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