Evaluation of Weighted Overlap-Tiling Strategies for 4× Super-Resolution under Blur Degradations with Applications to Gaussian and Motion Blur in 4K Images

Ziyue Wang

doi:10.54254/2755-2721/2026.TJ29616

Applied and Computational EngineeringOpen access

Evaluation of Weighted Overlap-Tiling Strategies for 4× Super-Resolution under Blur Degradations with Applications to Gaussian and Motion Blur in 4K Images

Research Article

Open Access

Evaluation of Weighted Overlap-Tiling Strategies for 4× Super-Resolution under Blur Degradations with Applications to Gaussian and Motion Blur in 4K Images

Ziyue Wang ^1*

¹ University of California

^*Corresponding author: ziyue649@ucsb.edu

Published on 19 November 2025

ACE Vol.207

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-539-4

ISBN (Online): 978-1-80590-540-0

Download Cover

Abstract

Single-image super-resolution (SR) has achieved strong performance on benchmark datasets with deep learning methods. However, applying SR to 4K images remains challenging due to GPU memory limits, inference speed, and the presence of visual artifacts at tile boundaries. A practical solution is overlap-tiling with weighted blending, which suppresses seams by smoothly merging patches. While widely used, the robustness of seamless SR under real degradations such as defocus or motion blur has not been systematically analyzed. This paper proposes a reproducible pipeline for seamless 4× SR on 4K images using a U-Net backbone combined with overlap-tiling and three weighting strategies: linear, Hann, and Gaussian. Synthetic Gaussian and motion blur with varying intensities are applied to test robustness. Extensive experiments demonstrate that larger overlaps improve seam suppression, and weighting profiles trade off differently between fidelity and runtime. Hann windows generally yield higher PSNR and SSIM, while Gaussian provides more stable results under strong blur. A Pareto analysis further highlights the balance between quality and efficiency. These findings establish overlap-tiling with proper blending as a practical and robust approach for real-world high-resolution SR applications.

Keywords:

Super-Resolution (SR), Overlap-Tiling, Weighted Blending, Image Restoration, 4K High-Resolution

View PDF

References

[1]. Farsiu, S., Robinson, M. D., Elad, M., & Milanfar, P. (2004). Fast and robust multiframe super resolution. IEEE transactions on image processing, 13(10), 1327-1344.

[2]. Shao, W. Z., & Elad, M. (2015). Simple, accurate, and robust nonparametric blind super-resolution. In Image and Graphics: 8th International Conference, ICIG 2015, Tianjin, China, August 13–16, 2015, Proceedings, Part III (pp. 333-348). Cham: Springer International Publishing.

[3]. Zhang, K., Liang, J., Van Gool, L., & Timofte, R. (2021). Designing a practical degradation model for deep blind image super-resolution. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 4791-4800).

[4]. Reina, G. A., Panchumarthy, R., Thakur, S. P., Bastidas, A., & Bakas, S. (2020). Systematic evaluation of image tiling adverse effects on deep learning semantic segmentation. Frontiers in neuroscience, 14, 65.

[5]. Buglakova, E., Archit, A., D'Imprima, E., Mahamid, J., Pape, C., & Kreshuk, A. (2025). Tiling artifacts and trade-offs of feature normalization in the segmentation of large biological images. arXiv preprint arXiv: 2503.19545.

[6]. Kim, S. Y., Sim, H., & Kim, M. (2021). Koalanet: Blind super-resolution using kernel-oriented adaptive local adjustment. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 10611-10620).

[7]. Ma, Z., Liao, R., Tao, X., Xu, L., Jia, J., & Wu, E. (2015). Handling motion blur in multi-frame super-resolution. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 5224-5232).

[8]. Zhang, X., Dong, H., Hu, Z., Lai, W. S., Wang, F., & Yang, M. H. (2018). Gated fusion network for joint image deblurring and super-resolution. arXiv preprint arXiv: 1807.10806.

[9]. Liu, L., Duan, J., Fu, X., Peng, W., & Liu, L. (2025). Unified 3D Gaussian splatting for motion and defocus blur reconstruction. Visual Informatics, 100270.

[10]. Zou, R., Pollefeys, M., & Rozumnyi, D. (2024). Retrieval Robust to Object Motion Blur. In European Conference on Computer Vision (pp. 251-268). Cham: Springer Nature Switzerland.

References

[1]. Farsiu, S., Robinson, M. D., Elad, M., & Milanfar, P. (2004). Fast and robust multiframe super resolution. IEEE transactions on image processing, 13(10), 1327-1344.

[8]. Zhang, X., Dong, H., Hu, Z., Lai, W. S., Wang, F., & Yang, M. H. (2018). Gated fusion network for joint image deblurring and super-resolution. arXiv preprint arXiv: 1807.10806.

[9]. Liu, L., Duan, J., Fu, X., Peng, W., & Liu, L. (2025). Unified 3D Gaussian splatting for motion and defocus blur reconstruction. Visual Informatics, 100270.

[10]. Zou, R., Pollefeys, M., & Rozumnyi, D. (2024). Retrieval Robust to Object Motion Blur. In European Conference on Computer Vision (pp. 251-268). Cham: Springer Nature Switzerland.

Cite this article

Wang,Z. (2025). Evaluation of Weighted Overlap-Tiling Strategies for 4× Super-Resolution under Blur Degradations with Applications to Gaussian and Motion Blur in 4K Images. Applied and Computational Engineering,207,20-29.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-SPML 2026 Symposium: The 2nd Neural Computing and Applications Workshop 2025

ISBN: 978-1-80590-539-4(Print) / 978-1-80590-540-0(Online)

Editor: Marwan Omar, Guozheng Rao

Conference website: https://www.confspml.org/tianjin.html

Conference date: 21 December 2025

Series: Applied and Computational Engineering

Volume number: Vol.207

ISSN: 2755-2721(Print) / 2755-273X(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:

1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.

2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.

3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).