Transpose convolution based model for super-resolution image reconstruction
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence
Link
https://link.springer.com/content/pdf/10.1007/s10489-022-03745-4.pdf
Reference29 articles.
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2. Schulter S, Leistner C, Bischof H (2015) Fast and accurate image upscaling with super-resolution forests. Proceedings IEEE computer society conference on computer vision and pattern recognition 07-12-June:3791–3799. https://doi.org/10.1109/CVPR.2015.7299003
3. Yang CY, Yang MH (2013) Fast direct super-resolution by simple functions. Proc IEEE Int Conf Comput Vis, 561–568. https://doi.org/10.1109/ICCV.2013.75
4. Timofte R, De Smet V, Van Gool L (2015) A+: Adjusted anchored neighborhood regression for fast super-resolution. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics) 9006:111–126. https://doi.org/10.1007/978-3-319-16817-3_8
5. Sahito F, Zhiwen P, Ahmed J, Memon RA (2019) Wavelet-integrated deepnetworks for single image super-resolution. Electronics 8:553
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