Unifying Dimensions : A Linear Adaptive Mixer for Lightweight Image Super-Resolution
Window-based Transformers have demonstrated outstanding performance in super-resolution due to their adaptive modeling capabilities through local self-attention (SA). However, they exhibit higher computational complexity and inference latency than convolutional neural networks. In this paper, we fir...
| Publié dans: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - PP(2025) vom: 17. Okt. |
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| Format: | Article en ligne |
| Langue: | English |
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2025
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| Accès à la collection: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society |
| Sujets: | Journal Article |
| Accès en ligne |
Volltext |