Abstract
In recent years, methods based on convolutional neural network (CNN) have been the mainstream in single image super-resolution (SISR). Although these methods have achieved excellent performance, the massive amount of model parameters and heavy computation limit their application. On the other hand, channel attention (CA) mechanism, which can enhance network performance, has also been widely used in SR task recently. However, the channel attention mechanism is introduced from high-level vision tasks to the SR task. The original design of this mechanism doesn’t consider the specificity of the SR task. To address these issues, we propose a lightweight expansion and distillation residual network (EDRN) for image super-resolution. Specifically, through the diverse use of different feature channels and different convolution kernel sizes, our network can effectively reduce the amount of parameters while achieving superior performance. To further explore the potential of channel-wise attention in the SR task, we develop a novel plug-and-play local channel attention enhancement strategy (LCAES) to make the network better use the characteristics of local features of the image. Furthermore, comprehensive quantitative and qualitative evaluations demonstrate that the proposed method performs favorably against state-of-the-art SR algorithms in terms of visual quality, reconstruction accuracy, and parameter amount.
This work was supported in part by the National Natural Science Foundation of China under Grant 61972305, 61871308, in part by the Natural Science Basic Research Plan in Shaanxi Province of China 2019JM-090, 2019JM-426.
The first author Yunchu Yang is a M.D. candidate.
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References
Ahn, N., Kang, B., Sohn, K.-A.: Fast, accurate, and lightweight super-resolution with cascading residual network. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11214, pp. 256–272. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01249-6_16
Bevilacqua, M., Roumy, A., Guillemot, C., Alberi-Morel, M.-L.: Low-complexity single-image super-resolution based on nonnegative neighbor embedding. In: Proceedings of the British Machine Vision Conference (BMVC), pp. 135.1–135.10 (2012)
Dong, C., Loy, C.C., Tang, X.: Accelerating the super-resolution convolutional neural network. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 391–407. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_25
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)
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7132–7141 (2018)
Huang, J.B., Singh, A., Ahuja, N.: Single image super-resolution from transformed self-exemplars. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5197–5206 (2015)
Hui, Z., Gao, X., Yang, Y., Wang, X.: Lightweight image super-resolution with information multi-distillation network. In: ACM Multimedia Conference on Multimedia Conference (ACMMM), pp. 2024–2032 (2019)
Hui, Z., Wang, X., Gao, X.: Fast and accurate single image super-resolution via information distillation network. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 723–731 (2018)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning (ICML), pp. 448–456 (2015)
Kim, J., Kwon Lee, J., Mu Lee, K.: Accurate image super-resolution using very deep convolutional networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1646–1654 (2016)
Kim, J., Kwon Lee, J., Mu Lee, K.: Deeply-recursive convolutional network for image super-resolution. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1637–1645 (2016)
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Lai, W.S., Huang, J.B., Ahuja, N., Yang, M.H.: Deep Laplacian pyramid networks for fast and accurate super-resolution. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5835–5843 (2017)
Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 105–114 (2017)
Lim, B., Son, S., Kim, H., Nah, S., Lee, K.M.: Enhanced deep residual networks for single image super-resolution. In: The IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1132–1140 (2017)
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. In: The IEEE International Conference on Computer Vision (ICCV), pp. 416–423 (2001)
Matsui, Y., Ito, K., Aramaki, Y., Fujimoto, A., Ogawa, T., Yamasaki, T., Aizawa, K.: Sketch-based manga retrieval using manga109 dataset. Multimed. Tools Appl. 76(20), 21811–21838 (2017)
Shi, W., et al.: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1874–1883 (2016)
Tai, Y., Yang, J., Liu, X.: Image super-resolution via deep recursive residual network. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2790–2798 (2017)
Tai, Y., Yang, J., Liu, X., Xu, C.: MemNet: a persistent memory network for image restoration. In: The IEEE International Conference on Computer Vision (ICCV), pp. 4549–4557 (2017)
Timofte, R., Agustsson, E., Van Gool, L., Yang, M.H., Zhang, L.: NTIRE 2017 challenge on single image super-resolution: methods and results. In: The IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1110–1121 (2017)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P., et al.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. (TIP) 13(4), 600–612 (2004)
Zeyde, R., Elad, M., Protter, M.: On single image scale-up using sparse-representations. In: International Conference on Curves and Surfaces, pp. 711–730 (2012)
Zhang, K., Zuo, W., Zhang, L.: Learning a single convolutional super-resolution network for multiple degradations. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3262–3271 (2018)
Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., Fu, Y.: Image super-resolution using very deep residual channel attention networks. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 294–310. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_18
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Yang, Y., Wang, X., Gao, X., Hui, Z. (2020). Lightweight Image Super-resolution with Local Attention Enhancement. In: Peng, Y., et al. Pattern Recognition and Computer Vision. PRCV 2020. Lecture Notes in Computer Science(), vol 12305. Springer, Cham. https://doi.org/10.1007/978-3-030-60633-6_18
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