HWT-SRNet: Heterogeneous Windows Transformer Network for Image Super-Resolution

LU Di; DANG Anyuan

doi:10.11999/JEIT250868

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LU Di, DANG Anyuan. HWT-SRNet: Heterogeneous Windows Transformer Network for Image Super-Resolution[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250868

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LU Di, DANG Anyuan. HWT-SRNet: Heterogeneous Windows Transformer Network for Image Super-Resolution[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250868

LU Di, DANG Anyuan. HWT-SRNet: Heterogeneous Windows Transformer Network for Image Super-Resolution[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250868

Citation:

LU Di, DANG Anyuan. HWT-SRNet: Heterogeneous Windows Transformer Network for Image Super-Resolution[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250868

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HWT-SRNet: Heterogeneous Windows Transformer Network for Image Super-Resolution

doi: 10.11999/JEIT250868 cstr: 32379.14.JEIT250868

LU Di^{1, 2
,
,},
DANG Anyuan^{1, 2}

1.
School of Measurement and Control Technology and Communication Engineering, Harbin University of Science and Technology, Harbin 150080, China
2.
Heilongjiang Province Key Laboratory of Pattern Recognition and Information Perception, Harbin University of Science and Technology, Harbin 150080, China

Accepted Date: 2026-05-29
Rev Recd Date: 2026-05-29

Available Online: 2026-06-08

Abstract

Abstract

In the era of big data, image quality varies greatly, making the reconstruction of high-resolution images from low-quality inputs a critical task in computer vision. Existing super-resolution methods based on window self-attention, such as SwinIR, encounter limitations in receptive field expansion and insufficient ability to capture high-frequency details. These shortcomings reduce their effectiveness in reconstructing fine image structures, thereby necessitating further improvements. To overcome these challenges, this study proposes the Heterogeneous Windows Transformer Network for Image Super-Resolution (HWT-SRNet), a novel architecture built upon SwinIR. By integrating innovative module designs, HWT-SRNet enhances the extraction of high-frequency details while simultaneously expanding the receptive field, offering a more advanced solution for super-resolution tasks. Methods Building upon the Swin IR framework, this study incorporates two key modules to optimize super-resolution reconstruction performance:(1) Heterogeneous Windows Transformer Block (HWTB): Traditional window-based self-attention mechanisms suffer from a constrained receptive field, limiting their ability to capture long-range dependencies. To overcome this limitation, HWTB alternates between square windows and pale windows, preserving local feature extraction while significantly expanding the receptive field. This alternating mechanism enables the network to better model both fine-grained details and global structural information, improving the overall image reconstruction quality. The choice of window size and alternation frequency is optimized to trade off between computational efficiency and feature extraction.(2) High-Frequency Prior Extraction Network (HFPEN): Transformer-based super-resolution models often struggle with capturing high-frequency details due to their inherent bias towards low-frequency components. To mitigate this issue, the HFPEN module is introduced to explicitly extract high-frequency prior information from images using a Gaussian Difference of Gaussian (DoG) filter. The DoG filter emphasizes high-frequency details, including edges and textures, by computing the difference between a lightly blurred image (containing mid-frequency information) and a more heavily blurred one (capturing low-frequency information).This high-frequency information is then fused with the heterogeneous window attention mechanism, allowing HWT-SRNet to enhance fine details while maintaining structural coherence. The DoG filter is applied in the spatial domain, enabling the model to effectively capture and reconstruct sharp edges and textures without the need for frequency-domain transformations. This approach ensures that the network can focus on high-frequency features while preserving the overall image structure. Results and Discussions To thoroughly assess the effectiveness of HWT-SRNet, we performed experiments on several widely used benchmark datasets, namely Set5, Set14, BSD100, Urban100, and Manga109. Our method was compared with representative state-of-the-art approaches, including ACT, ART, and CAT.,The results demonstrate its superior performance across key evaluation metrics (see Table 1 for detailed comparisons). Specifically, HWT-SRNet achieves improvements in PSNR ranging from 0.10 dB to 0.37 dB compared to baseline models, demonstrating its effectiveness in enhancing image quality. Additionally, structural similarity (SSIM) scores also exhibit consistent improvement, indicating better perceptual quality and more visually pleasing reconstructions. Qualitative results further confirm that HWT-SRNet is capable of restoring sharper edges, preserving textures, and reducing blurring artifacts compared to existing methods. To further validate the contribution of each component in HWT-SRNet, we conducted ablation studies to analyze the impact of the Heterogeneous Windows Transformer Block (HWTB) and the High-Frequency Prior Extraction Network (HFPEN) (see Table 2 for ablation results).These advantages stem from the synergistic effect of heterogeneous window attention mechanisms and high-frequency prior extraction, which enable the network to effectively balance local feature refinement and global contextual understanding. By leveraging the alternating self-attention mechanisms and high-frequency prior extraction, HWT-SRNet provides a highly efficient solution for expanding the receptive field and improving high-frequency detail reconstruction. Conclusion Considering the limitations of existing super-resolution algorithms, this paper proposes a novel Heterogeneous Windows Transformer Network (HWT-SRNet), designed to improve image reconstruction quality by addressing challenges in receptive field expansion and high-frequency detail capture. The integration of heterogeneous window attention mechanisms and high-frequency prior feature extraction allows the model to achieve a more effective fusion of local and global features, leading to superior performance in both PSNR and SSIM. Experimental results confirm that HWT-SRNet surpasses existing state-of-the-art methods, providing a more efficient and accurate solution for super-resolution tasks. However, this study does not specifically explore the model's adaptability to noise interference in real-world scenarios. Future research can focus on further optimizing HWT-SRNet’s robustness to noisy and degraded inputs, improving its applicability to practical image restoration tasks in diverse environments. Additionally, the model's performance on specialized datasets, such as medical or satellite images, remains to be explored, which could further validate its generalization capabilities.
- Super-resolution reconstruction,
- Transformer,
- Alternating windows,
- High-frequency prior

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References(23)

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