DSFormer: Leveraging Transformer with Cross-Modal Attention for Temporal Consistency in Low-Light Video Enhancement
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-5612-4_3
Reference24 articles.
1. Zhang, F., Li, Y., You, S., Fu, Y.: Learning temporal consistency for low light video enhancement from single images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4967–4976 (2021)
2. Dosovitskiy, A., et~al.: An image is worth 16x16 words: Transformers for image recognition at scale (2020). arXiv preprint arXiv:2010.11929
3. Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10012–10022 (2021)
4. Zheng, S., Gupta, G.: Semantic-guided zero-shot learning for low-light image/video enhancement. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 581–590 (2022)
5. Azizi, Z., et al.: Salve: self-supervised adaptive low-light video enhancement. APSIPA Trans. Signal Inf. Proc. 12(4) (2022)
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