Abstract
Lightweight image super-resolution (SR) networks are of great significance for practical applications. Presently, there are several SR methods based on deep learning with excellent performance, but their memory and computation costs hinder practical applications. In this paper, we propose a down-up sampling continuous mutual affine super-resolution network (DUSCMAnet) to solve above problems. Moreover, we propose a classification-based SR algorithm based on image statistical features (TSClassSR-DUSCMAnet) for accelerating SR networks on large images (2K–8K). The proposed algorithm first decomposes the large images into small sub-images, then uses a Class-Module to classify sub-images into different classes according to the difficulty of reconstruction, then use a SR-Module to perform SR for different classes. The Class-Module is composed of a support vector machine (SVM) based on image statistical features, and the SR-Module is composed of our proposed DUSCMAnet, a lightweight SR network. After classifying, a majority of sub-images will pass through lighter networks, thus the computational cost can be significantly reduced. Experiments show that our DUSCMAnet is superior to the existing lightweight SR models in terms of time performance and also has competitive SR performance. Our TSClassSR-DUSCMAnet can help DUSCMAnet save up to 63% FLOPs on DIV8K datasets.
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References
Agustsson, E., Timofte, R.: Ntire 2017 challenge on single image super-resolution: Dataset and study. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 126–135 (2017)
Ahn, N., Kang, B., Sohn, K-A.: Fast, accurate, and lightweight super-resolution with cascading residual network. In: Proceedings of the European conference on Computer Vision (ECCV), pp. 252–268 (2018)
Chen, B., Lin, M., Sheng, K., Zhang, M., Chen, P., Li, K., et al.: Arm: Any-time super-resolution method. In: Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27 2022, Proceedings, Part XIX, pp. 254–270 (2022). https://doi.org/10.1007/978-3-031-19800-7_15
Dong, C., Loy, C.C., Tang, X.: Accelerating the super-resolution convolutional neural network. In: European Conference on Computer Vision, pp. 391–407 (2016). https://doi.org/10.1007/978-3-319-46475-6_25
Huang, H., Shen, L., He, C., Dong, W., Liu, W.: Differentiable neural architecture search for extremely lightweight image super-resolution. IEEE Trans. Circ. Syst. Video Technol. (2022)
Hui, Z., Wang, X., Gao, X.: Fast and accurate single image super-resolution via information distillation network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 723–731 (2018)
Kim, J., Lee, J.K., Lee, K.M.: Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1646–1654 (2016)
Kong, X., Zhao, H., Qiao, Y., Dong, C.: Classsr: A general framework to accelerate super-resolution networks by data characteristic. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12016–12025 (2021)
Lai, W.-S., Huang, J.-B., Ahuja, N., Yang, M.-H.: 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 (2017)
Li, Z., Liu, Y., Chen, X., Cai, H., Gu, J., Qiao, Y., et al.: Blueprint separable residual network for efficient image super-resolution. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 833–843 (2022)
Liang, J., Sun, G., Zhang, K., Van Gool, L., Timofte, R.: Mutual affine network for spatially variant kernel estimation in blind image super-resolution. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 4096–4105 (2021)
Lim, B., Son, S., Kim, H., Nah, S., Lee, M.K.: 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 (2017)
Liu, J., Tang, J., Wu, G.: Residual feature distillation network for lightweight image super-resolution. In: European Conference on Computer Vision, pp. 41–55 (2020). https://doi.org/10.1007/978-3-030-67070-2_2
Liu J, Zhang W, Tang Y, Tang J, Wu G.: Residual feature aggregation network for image super-resolution. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2359–2368 (2020)
Zhang, K., Danelljan, M., Li, Y., Timofte, R., Liu, J., Tang, J., et al.: Aim 2020 challenge on efficient super-resolution: Methods and results. In: Computer Vision–ECCV 2020 Workshops: Glasgow, UK, August 23–28 2020, Proceedings, Part III 16, pp. 5–40 (2020). https://doi.org/10.1007/978-3-030-67070-2_1
Zhang, K., Zuo, W., Zhang, L.: Learning a single convolutional super-resolution network for multiple degradations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3262–3271 (2018)
Acknowledgments
This work was supported by the National Natural Science Foundation of China under Grant 62176161, and the Scientific Research and Development Foundations of Shenzhen under Grant JCYJ20220818100005011 and 20200813144831001.
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Zhong, X., Luo, J. (2023). Classification-Based and Lightweight Networks for Fast Image Super Resolution. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14255. Springer, Cham. https://doi.org/10.1007/978-3-031-44210-0_12
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