Burst super-resolution with adaptive feature refinement and enhanced group up-sampling
Author:
Funder
Samsung
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence
Link
https://link.springer.com/content/pdf/10.1007/s10489-023-05127-w.pdf
Reference42 articles.
1. Bhat G, Danelljan M, Van Gool L et al (2021a) Deep burst super-resolution. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 9209–9218
2. Bhat G, Danelljan M, Yu F et al (2021b) Deep reparametrization of multi-frame super-resolution and denoising. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 2460–2470
3. Brooks T, Mildenhall B, Xue T et al (2019) Unprocessing images for learned raw denoising. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 11036–11045
4. Chen L, Chu X, Zhang X et al (2022) Simple baselines for image restoration. In: European conference on computer vision. Springer, pp 17–33
5. Chen Y, Liu L, Phonevilay V et al (2021) Image super-resolution reconstruction based on feature map attention mechanism. Appl Int 51:4367–4380
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