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Single image super-resolution using global enhanced upscale network

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Abstract

Current works on super-resolution have obtained satisfactory results since the advance of the convolution neural network. Nevertheless, most previous works use one network for one integer scale factor so ignore the super-resolution of the arbitrary scale factor. In this work, we propose a novel approach called Global Enhanced Upscale Network (GEUN) to tackle super-resolution with a single model adapting the arbitrary scale factor. In our GEUN, we propose the Global Enhanced Upscale module to replace the conventional upscale module. Our GEUN can upscale low-resolution images with an arbitrary scale factor through only one model. Extensive experimental results demonstrate the superiority of our GEUN.

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References

  1. Li B, Hong C, Shan S, Chen X (2009) Low-resolution face recognition via coupled locality preserving mappings. IEEE Signal Proc Lett 17(1):20–23

    Google Scholar 

  2. Dai T, Cai J, Zhang Y, Xia S-T, Zhang L (2019) Second-order attention network for single image super-resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 11065–11074

  3. Dong C, Loy CC, He K, Tang X (2015) Image super-resolution using deep convolutional networks. IEEE Trans Pattern Anal Mach Intell 38(2):295–307

    Article  Google Scholar 

  4. Chao D, Chen CL, Xiaoou T (2016) Accelerating the super-resolution convolutional neural network. In: European conference on computer vision, pp 391–407. Springer

  5. Fang F, Li J, Zeng T (2020) Soft-edge assisted network for single image super-resolution. IEEE Trans Image Process 29:4656–4668

    Article  Google Scholar 

  6. Goodfellow I, Pouget-Abadie J, Mirza M, Bing X, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Advances in neural information processing systems, pp 2672–2680

  7. Xuecai H, Haoyuan M, Zhang X, Wang Z, Tan T, Sun J (2019) Meta-sr: a magnification-arbitrary network for super-resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1575–1584

  8. Younghyun J, Oh SW, Kang J, Kim SJ (2018) Deep video super-resolution network using dynamic upsampling filters without explicit motion compensation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3224–3232

  9. Kennedy JA, Israel O, Frenkel A, Bar-Shalom R, Azhari H (2006) Super-resolution in pet imaging. IEEE Trans Med Imaging 25(2):137–147

    Article  Google Scholar 

  10. Kim J, Lee JK, Lee KM (2016) Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1646–1654

  11. Kim J, Lee JK, Lee KM (2016) Deeply-recursive convolutional network for image super-resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1637–1645

  12. Lai W-S, Huang J-B, Ahuja N, Yang M-H (2017) Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 624–632

  13. Ledig C, Theis L, Huszár F., Caballero J, Cunningham A, Acosta A, Aitken A, Tejani A, Totz J, Wang Z et al (2017) Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4681–4690

  14. Lim B, Son S, Kim H, Nah S, Lee KM (2017) Enhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 136–144

  15. Liu X, Zhu X, Li M, Wang L, Zhu E, Liu T, Kloft M, Shen D, Yin J, Gao W (2019) Multiple kernel k k-means with incomplete kernels. IEEE Trans Pattern Anal Mach Intell 42(5):1191–1204

    Google Scholar 

  16. Shi W, Caballero J, Huszár F, Totz J, Aitken AP, Bishop R, Rueckert D, Wang Z (2016) Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1874–1883

  17. Tai Y, Yang J, Liu X (2017) Image super-resolution via deep recursive residual network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3147–3155

  18. Tai Y, Yang J, Liu X, Memnet CX (2017) A persistent memory network for image restoration. In: Proceedings of the IEEE international conference on computer vision, pp 4539–4547

  19. Timofte R, Agustsson E, Gool LV, Yang Ming-Hsuan, Zhang L (2017) Ntire 2017 challenge on single image super-resolution methods and results. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 114–125

  20. Wang R, Gong M, Tao D (2020) Receptive field size versus model depth for single image super-resolution. IEEE Trans Image Process 29:1669–1682

    Article  MathSciNet  Google Scholar 

  21. Wang X, Ke Y, Dong C, Loy CC (2018) Recovering realistic texture in image super-resolution by deep spatial feature transform. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 606–615

  22. Xiao Y, Xi Y, Gao Q (2020) Infrared handprint image restoration algorithm based on apoptotic mechanism. IEEE Access 8:47334–47343

    Article  Google Scholar 

  23. Zhang Y, Li K, Li K, Wang L, Zhong B, Yun F (2018) Image super-resolution using very deep residual channel attention networks. In: Proceedings of the European conference on computer vision (ECCV), pp 286–301

  24. Zhang Y, Li K, Li K, Zhong B, Fu Y (2019) Residual non-local attention networks for image restoration. arXiv:1903.10082

  25. Zhang Y, Tian Y, Kong Y, Zhong B, Yun F (2018) Residual dense network for image super-resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2472–2481

  26. Zhang Y (2002) Problems in the fusion of commercial high-resolution satelitte as well as landsat 7 images and initial solutions. Int Arch Photogramm Remote Sens Spatial Inf Sci 34(4):587– 592

    Google Scholar 

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  1. School of Electronic Information, Zhuhai College of Jilin University, Zhuhai, Guangdong, 519000, China

    Xiaobiao Du

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  1. Xiaobiao Du
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Du, X. Single image super-resolution using global enhanced upscale network. Appl Intell 52, 2813–2819 (2022). https://doi.org/10.1007/s10489-021-02565-2

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