Structural Similarity-Inspired Unfolding for Lightweight Image Super-Resolution

Major efforts in data-driven image super-resolution (SR) primarily focus on expanding the receptive field of the model to better capture contextual information. However, these methods are typically implemented by stacking deeper networks or leveraging transformer-based attention mechanisms, which co...

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Vydané v:	IEEE transactions on image processing Ročník 34; s. 3861 - 3872
Hlavní autori:	Ni, Zhangkai, Zhang, Yang, Yang, Wenhan, Wang, Hanli, Wang, Shiqi, Kwong, Sam
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.01.2025
Predmet:	Complexity theory Computational modeling Electronic mail Encoding Estimation Feature extraction Image reconstruction Image super-resolution light-weight Optimization paradigm unfolding sparse attention Superresolution Transformers
ISSN:	1057-7149, 1941-0042, 1941-0042
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