Abstract
This article introduces an innovative strategy for improving image super-resolution through the utilization of Super-Resolution Generative Adversarial Networks (SRGANs). By harmoniously incorporating perceptual loss functions and refining the model's structure, the proposed technique strives to strike an equilibrium between quantitative measurements and perceptual authenticity. Empirical assessments conducted on benchmark datasets demonstrate that the resulting high-resolution images, generated through this method, showcase exceptional quality and perceptual fidelity in comparison to conventional methods. The integration of SRGANs represents a noteworthy leap in the domain of image resolution enhancement, holding the potential to deliver visually captivating and perceptually plausible high-resolution images across a wide spectrum of applications.
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References
Nasrollahi, K., Moeslund, T.B.: Super-resolution: a comprehensive survey 25(6) (2014)
Bevilacqua, M., Roumy, A., Guillemot, C., Morel, M.L.A.: Low-complexity single-image super-resolution based on nonnegative neighbor embedding. BMVC 2012—Electron. Proc. Br. Mach. Vis. Conf. (Ml), 1–10 (2012). https://doi.org/10.5244/C.26.135
Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2016). https://doi.org/10.1109/TPAMI.2015.2439281
Chen, R., Qu, Y., Li, C., Zeng, K., Xie, Y., Li, C.: Single-image super-resolution via joint statistic models-guided deep auto-encoder network. Neural Comput. Appl. 32(9), 4885–4896 (2020). https://doi.org/10.1007/s00521-018-3886-2
Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR, vol. 2017, pp. 105–114 (2017). https://doi.org/10.1109/CVPR.2017.19
Goodfellow, I.J., et al.: Generative adversarial nets. Adv. Neural. Inf. Process. Syst. 3(January), 2672–2680 (2014). https://doi.org/10.3156/jsoft.29.5_177_2
October, M.: A Wavelet Tour of Signal Processing (2008)
“Ten Lectures of Wavelets”
Tai, Y., Yang, J., Liu, X., Xu, C.: MemNet: a persistent memory network for image restoration
Woo, S., Park, J., Lee, J., Kweon, S.: CBAM: convolutional block attention module
Wang, X., Yu, K., Wu, S., Gu, J., Liu, Y.: ESRGAN : enhanced super-resolution generative adversarial networks, pp. 1–16
Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of GANs for improved quality, stability, and variation (2017), [Online]. Available: http://arxiv.org/abs/1710.10196
Durdanovic, I., Graf, H.P.: P f e c n (2016), 1–13 (2017)
Bai, L., et al.: Multispectral U-Net : a semantic segmentation model using multispectral bands fusion mechanism for landslide detection, pp. 73–76 (2022)
Tao, Y.: Super-resolution restoration of spaceborne ultra-high-resolution images using the UCL OpTiGAN system (2021)
Agustsson, E., Timofte, R.: NTIRE 2017 challenge on single image super-resolution: dataset and study. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Work. 2017-July, 1122–1131 (2017). https://doi.org/10.1109/CVPRW.2017.150
Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. Proc. IEEE Int. Conf. Comput. Vis. 2, 416–423 (2001). https://doi.org/10.1109/ICCV.2001.937655
Zeyde, R., Elad, M., Protter, M.: On single image scale-up using sparse-representations. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics) 6920 LNCS(1), 711–730, 2012. https://doi.org/10.1007/978-3-642-27413-8_47
Qu, H., Yi, H., Shi, Y., Lan, J.: DVDR-SRGAN : differential value dense residual (2023)
Li, W., Zhou, K., Qi, L., Lu, L., Lu, J.: Best-buddy GANs for Highly detailed image super-resolution (2019)
Ma, C., Rao, Y., Cheng, Y., Chen, C., Lu, J., Zhou, J.: Structure-preserving super resolution with gradient guidance, pp. 7769–7778
Farhaoui, Y.: All, big data mining and analytics 6(3), I–II (2023). https://doi.org/10.26599/BDMA.2022.9020045
Farhaoui, Y.: Big data analytics applied for control systems. In: Lecture Notes in Networks and Systems, vol. 25, pp. 408–415 (2018). https://doi.org/10.1007/978-3-319-69137-4_36
Farhaoui, Y., et al.: Big data mining and analytics 5(4), I–II (2022). https://doi.org/10.26599/BDMA.2022.9020004
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Manaa, M.J., Abbas, A.R., Shakur, W.A. (2024). Improving the Resolution of Images Using Super-Resolution Generative Adversarial Networks. In: Farhaoui, Y., Hussain, A., Saba, T., Taherdoost, H., Verma, A. (eds) Artificial Intelligence, Data Science and Applications. ICAISE 2023. Lecture Notes in Networks and Systems, vol 837. Springer, Cham. https://doi.org/10.1007/978-3-031-48465-0_9
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