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Light-weight super-resolution (SR) models have received considerable attention for their serviceability in mobile devices.
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Shi, W., Caballero, J., Huszár, F., Totz, J., Aitken, A.P., Bishop, R., Rueckert, D., Wang, Z.: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 1874–1883 (2016)
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2017
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Tong, T., Li, G., Liu, X., Gao, Q.: Image super-resolution using dense skip connections. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 4799–4807 (2017)
2017
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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)
2018
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2018
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Haris, M., Shakhnarovich, G., Ukita, N.: Deep back-projection networks for super-resolution. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 1664–1673 (2018)
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Jacob, B., Kligys, S., Chen, B., Zhu, M., Tang, M., Howard, A., Adam, H., Kalenichenko, D.: Quantization and training of neural networks for efficient integer-arithmetic-only inference. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 2704–2713 (2018)
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2018
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Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., Fu, Y.: Image super-resolution using very deep residual channel attention networks. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 286–301 (2018)
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Mei, Y., Fan, Y., Zhou, Y., Huang, L., Huang, T.S., Shi, H.: Image super-resolution with cross-scale non-local attention and exhaustive self-exemplars mining. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 5690–5699 (2020)
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Xin, J., Wang, N., Jiang, X., Li, J., Huang, H., Gao, X.: Binarized neural network for single image super resolution. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 91–107 (2020)
2020
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Jiang, X., Wang, N., Xin, J., Li, K., Yang, X., Gao, X.: Training binary neural network without batch normalization for image super-resolution. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI). pp. 1700–1707 (2021)
2021
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Magid, S.A., Zhang, Y., Wei, D., Jang, W.D., Lin, Z., Fu, Y., Pfister, H.: Dynamic high-pass filtering and multi-spectral attention for image super-resolution. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 4288–4297 (2021)
2021
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Mei, Y., Fan, Y., Zhou, Y.: Image super-resolution with non-local sparse attention. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 3517–3526 (2021)
2021
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Wang, H., Chen, P., Zhuang, B., Shen, C.: Fully quantized image super-resolution networks. In: Proceedings of the 29th ACM International Conference on Multimedia (ACMMM). pp. 639–647 (2021)
2021
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Zhang, R., Chung, A.C.: Medq: Lossless ultra-low-bit neural network quantization for medical image segmentation. Medical Image Analysis 73
2021
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Hong, C., Kim, H., Baik, S., Oh, J., Lee, K.M.: Daq: Channel-wise distribution-aware quantization for deep image super-resolution networks. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). pp. 2675–2684 (2022)
2022
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