Lightweight dynamic attention network for single thermal image super-resolution
Author:
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Signal Processing
Link
https://link.springer.com/content/pdf/10.1007/s11760-023-02886-4.pdf
Reference44 articles.
1. Chudasama, V., Patel, H., Prajapati, K., Upla, K.P., Ramachandra, R., Raja, K., Busch, C.: Therisurnet-a computationally efficient thermal image super-resolution network. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 86–87 (2020)
2. Zhang, H., Hu, Y., Yan, M., Ma, B.: Thermal image super-resolution via multi-path residual attention network. Signal Image Video, 1–9 (2022)
3. Yang, J., Wright, J., Huang, T.S., Ma, Y.: Image super-resolution via sparse representation. IEEE Trans. Image Process. 19(11), 2861–2873 (2010)
4. Wang, Z., Chen, J., Hoi, S.C.: Deep learning for image super-resolution: a survey. IEEE Trans. Pattern Anal. 43(10), 3365–3387 (2020)
5. Prajapati, K., Chudasama, V., Patel, H., Sarvaiya, A., Upla, K.P., Raja, K., Ramachandra, R., Busch, C.: Channel split convolutional neural network (chasnet) for thermal image super-resolution. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4368–4377 (2021)
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