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

Received Date: 2025-09-04
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. Reconstructing high-resolution images from low-quality inputs is therefore an important task in computer vision. Existing super-resolution methods based on window self-attention, such as SwinIR, have limited receptive fields and insufficient ability to capture high-frequency details. These limitations weaken the reconstruction of fine image structures. To address these issues, this study proposes the Heterogeneous Windows Transformer Network for Image Super-Resolution (HWT-SRNet), a new architecture built on SwinIR. Through targeted module design, HWT-SRNet improves high-frequency detail extraction while expanding the receptive field, providing an effective solution for image super-resolution. Methods Based on SwinIR, this study designs two key modules to improve super-resolution reconstruction. First, the Residual Heterogeneous Windows Transformer Block (RHWTB) alternates square windows and pale-shaped windows. This design preserves local feature extraction while expanding the receptive field, enabling the network to model both fine-grained details and global structural information. The window size and alternation frequency are optimized to balance computational efficiency and feature extraction. Second, the High-Frequency Prior Feature Extraction Network (HFPFEN) is used to compensate for the limited high-frequency modeling ability of Transformer-based super-resolution models. HFPFEN extracts high-frequency prior information from images using a Difference of Gaussians (DoG) filter. The DoG filter emphasizes edges and textures by computing the difference between lightly and heavily blurred images. The extracted high-frequency information is then fused with the heterogeneous window attention mechanism. This design allows HWT-SRNet to enhance fine details while maintaining structural coherence. Because the DoG filter is applied in the spatial domain, the model can capture and reconstruct sharp edges and textures without frequency-domain transformation. Results and Discussions Experiments are conducted on five widely used benchmark datasets: Set5, Set14, BSD100, Urban100, and Manga109. HWT-SRNet is compared with representative advanced methods, including ACT, ART, and CAT. The results show superior performance across key evaluation metrics (Table 1). Compared with baseline models, HWT-SRNet improves the Peak Signal-to-Noise Ratio (PSNR) by 0.10 dB to 0.37 dB, confirming its effect in improving image quality. The Structural Similarity Index Measure (SSIM) also shows consistent improvement, indicating better perceptual quality and more accurate reconstruction. Qualitative results further show that HWT-SRNet restores sharper edges, preserves textures more effectively, and reduces blurring artifacts. Ablation studies are conducted to evaluate the contributions of RHWTB and HFPFEN (Table 2). The results confirm that heterogeneous window attention and high-frequency prior extraction jointly improve local feature refinement and global context modeling. Therefore, HWT-SRNet provides an efficient solution for receptive field expansion and high-frequency detail reconstruction. Conclusion This paper proposes HWT-SRNet to address the limited receptive fields and insufficient high-frequency detail capture of existing super-resolution algorithms. By integrating heterogeneous window attention with high-frequency prior feature extraction, the model achieves more effective fusion of local and global features. Experimental results confirm that HWT-SRNet improves both PSNR and SSIM and outperforms representative advanced methods. However, this study does not specifically examine the model’s adaptability to noise interference in real-world scenarios. Future research can further improve the robustness of HWT-SRNet to noisy and degraded inputs and evaluate its generalization on specialized datasets, such as medical and satellite images.
- Super-resolution reconstruction,
- Transformer,
- Heterogeneous windows,
- High-frequency prior

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References(23)

References

[1]	DONG Chao, LOY C C, HE Kaiming, et al. Learning a deep convolutional network for image super-resolution[C]. The 13th European Conference on Computer Vision, Zurich, Switzerland, 2014: 184–199. doi: 10.1007/978-3-319-10593-2_13.
[2]	DONG Chao, LOY C C, and TANG Xiaoou. Accelerating the super-resolution convolutional neural network[C]. 14th European Conference on Computer Vision, Amsterdam, The Netherlands, 2016: 391–407. doi: 10.1007/978-3-319-46475-6_25.
[3]	ZHANG Yulun, LI Kunpeng, LI Kai, et al. Image super-resolution using very deep residual channel attention networks[C]. The 15th European Conference on Computer Vision, Munich, Germany, 2018: 294–310. doi: 10.1007/978-3-030-01234-2_18.
[4]	DOSOVITSKIY A, BEYER L, KOLESNIKOV A, et al. An image is worth 16x16 words: Transformers for image recognition at scale[C]. The 9th International Conference on Learning Representations, Vienna, Austria, 2021: 1–21.
[5]	LIU Ze, LIN Yutong, CAO Yue, et al. Swin transformer: Hierarchical vision transformer using shifted windows[C]. The 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, Canada, 2021: 9992–10002. doi: 10.1109/ICCV48922.2021.00986.
[6]	LIANG Jingyun, CAO Jiezhang, SUN Guolei, et al. SwinIR: Image restoration using Swin transformer[C]. The 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), Montreal, Canada, 2021: 1833–1844. doi: 10.1109/ICCVW54120.2021.00210.
[7]	CHEN Zheng, ZHANG Yulun, GU Jinjin, et al. Cross aggregation transformer for image restoration[C]. The 36th International Conference on Neural Information Processing Systems, New Orleans, USA, 2022: 1847.
[8]	SHI Jinpeng, LI Hui, LIU Tianle, et al. Image super-resolution using efficient striped window transformer[EB/OL]. https://arxiv.org/abs/2301.09869, 2023.
[9]	WU Sitong, WU Tianyi, TAN Haoru, et al. Pale transformer: A general vision transformer backbone with pale-shaped attention[C]. The 36th AAAI Conference on Artificial Intelligence, Palo Alto, USA, 2022: 2731–2739. doi: 10.1609/aaai.v36i3.20176.
[10]	WU Gang, JIANG Junjun, JIANG Kui, et al. Content-aware transformer for all-in-one image restoration[EB/OL]. https://arxiv.org/abs/2504.04869v1, 2025.
[11]	ZHANG Jiale, ZHANG Yulun, GU Jinjin, et al. Accurate image restoration with attention retractable transformer[C]. The Eleventh International Conference on Learning Representations, Kigali, Rwanda, 2023: 1–13.
[12]	CHEN Zheng, ZHANG Yulun, GU Jinjin, et al. Recursive generalization transformer for image super-resolution[C]. The Twelfth International Conference on Learning Representations, Vienna, Austria, 2024: 1–12.
[13]	CHU Shuchuan, DOU Zhichao, PAN J S, et al. HMANet: Hybrid multi-axis aggregation network for image super-resolution[C].The 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, USA, 2024: 6257–6266. doi: 10.1109/CVPRW63382.2024.00629.
[14]	YOO J, KIM T, LEE S, et al. Enriched CNN-transformer feature aggregation networks for super-resolution[C]. The 2023 IEEE/CVF Winter Conference on Applications of Computer Vision, Waikoloa, USA, 2023: 4945–4954. doi: 10.1109/WACV56688.2023.00493.
[15]	SI Chenyang, YU Weihao, ZHOU Pan, et al. Inception transformer[C]. The 36th International Conference on Neural Information Processing Systems, New Orleans, USA, 2022: 1707.
[16]	KORKMAZ C, TEKALP A M, and DOGAN Z. Training generative image super-resolution models by wavelet-domain losses enables better control of artifacts[C]. 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, USA, 2024: 5926–5936. doi: 10.1109/CVPR52733.2024.00566.
[17]	韩玉兰, 崔玉杰, 罗轶宏, 等. 基于密集残差和质量评估引导的频率分离生成对抗超分辨率重构网络[J]. 电子与信息学报, 2024, 46(12): 4563–4574. doi: 10.11999/JEIT240388. HAN Yulan, CUI Yujie, LUO Yihong, et al. Frequency separation generative adversarial super-resolution reconstruction network based on dense residual and quality assessment[J]. Journal of Electronics & Information Technology, 2024, 46(12): 4563–4574. doi: 10.11999/JEIT240388.
[18]	LI Ao, ZHANG Le, LIU Yun, et al. Feature modulation transformer: Cross-refinement of global representation via high-frequency prior for image super-resolution[C]. The 2023 IEEE/CVF International Conference on Computer Vision (ICCV), Paris, France, 2023: 12480–12490. doi: 10.1109/iccv51070.2023.01150.
[19]	YAO Hongdou, HAN Pengfei, WANG Xiaofen, et al. Super-resolution via hierarchical attention and detail enhancement transformer network[J]. Optics & Laser Technology, 2025, 188: 112836. doi: 10.1016/j.optlastec.2025.112836.
[20]	程德强, 袁航, 钱建生, 等. 基于深层特征差异性网络的图像超分辨率算法[J]. 电子与信息学报, 2024, 46(3): 1033–1042. doi: 10.11999/JEIT230179. CHENG Deqiang, YUAN Hang, QIAN Jiansheng, et al. Image super-resolution algorithms based on deep feature differentiation network[J]. Journal of Electronics & Information Technology, 2024, 46(3): 1033–1042. doi: 10.11999/JEIT230179.
[21]	寇旗旗, 刘规, 江鹤, 等. 基于多域信息增强的轻量级图像超分辨率网络[J]. 通信学报, 2025, 46(4): 144–159. doi: 10.11959/j.issn.1000-436x.2025059. KOU Qiqi, LIU Gui, JIANG He, et al. Lightweight image super-resolution network based on muti-domain information enhancement[J]. Journal on Communications, 2025, 46(4): 144–159. doi: 10.11959/j.issn.1000-436x.2025059.
[22]	WANG Xintao, XIE Liangbin, DONG Chao, et al. Real-ESRGAN: Training real-world blind super-resolution with pure synthetic data[C]. The 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), Montreal, Canada, 2021: 1905–1914. doi: 10.1109/ICCVW54120.2021.00217.
[23]	WANG Yufei, YANG Wenhan, CHEN Xinyuan, et al. SinSR: Diffusion-based image super-resolution in a single step[C]. The 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, USA, 2024: 25796–25805. doi: 10.1109/CVPR52733.2024.02437.