Exploring Deep Learning Strategies and Prospective Developments

Yi Sun

doi:10.62051/ke30fz44

Authors

Yi Sun

DOI:

https://doi.org/10.62051/ke30fz44

Keywords:

Single image super-resolution; deep learning; convolutional neural networks; generative adversarial networks; transformer.

Abstract

Single Image Super-Resolution (SISR) is a process designed to transform Low-Resolution (LR) images into High-Resolution (HR) counterparts. This technology finds critical applications in various sectors, including gaming, photography, and medical imaging. With the advent and widespread success of deep learning, this approach has been increasingly applied in the realm of SISR. Deep learning-based SISR models are primarily categorized into three types based on their nonlinear module structures: Convolutional Neural Network (CNN)-based models, Generative Adversarial Network (GAN)-based models, and Transformer-based models. This paper presents a comprehensive overview of several emblematic models within each category. An in-depth analysis and comparison of their structural nuances and experimental outcomes are provided. This comparison elucidates how enhancements in network architectures and refined loss function optimizations contribute substantially to advancements in performance. Concluding with an analysis of current models, the paper outlines potential avenues for future exploration and development in the field of SISR, indicating a promising trajectory for further technological advancements.

Downloads

Download data is not yet available.

References

Dong, C., Loy, C. C., He, K., & Tang, X. (2014). Learning a deep convolutional network for image super-resolution. In Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part IV 13 (pp. 184-199). Springer International Publishing.

Dong, C., Loy, C. C., & Tang, X. (2016). Accelerating the super-resolution convolutional neural network. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, the Netherlands, October 11-14, 2016, Proceedings, Part II 14 (pp. 391-407). Springer International Publishing.

Kim, J., Lee, J. K., & Lee, K. M. (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).

Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A., Acosta, A., ... & Shi, W. (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).

Lim, B., Son, S., Kim, H., Nah, S., & Mu Lee, K. (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).

Wang, X., Yu, K., Wu, S., Gu, J., Liu, Y., Dong, C., ... & Change Loy, C. (2018). Esrgan: Enhanced super-resolution generative adversarial networks. In Proceedings of the European conference on computer vision (ECCV) workshops (pp. 0-0).

Liang, J., Cao, J., Sun, G., Zhang, K., Van Gool, L., & Timofte, R. (2021). Swinir: Image restoration using swin transformer. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 1833-1844).

Lu, Z., Li, J., Liu, H., Huang, C., Zhang, L., & Zeng, T. (2022). Transformer for single image super-resolution. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 457-466).

Zheng, L., Zhu, J., Shi, J., & Weng, S. (2024). Efficient mixed transformer for single image super-resolution. Engineering Applications of Artificial Intelligence, 133, 108035.

Tong, T., Li, G., Liu, X., & GAO, Q. (2017). Image super-resolution using dense skip connections. In Proceedings of the IEEE international conference on computer vision (pp. 4799-4807).

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

Niu, B., Wen, W., Ren, W., Zhang, X., Yang, L., Wang, S. ... & Shen, H. (2020). Single image super-resolution via a holistic attention network. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XII 16 (pp. 191-207). Springer International Publishing.

Zhang, S., Liang, G., Pan, S., & Zheng, L. (2018). A fast medical image super resolution method based on deep learning network. IEEE Access, 7, 12319-12327.