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Super-Resolution of Remote Sensing Images via the Parallel Lattice Attention Network

Deep learning-based super-resolution models often struggle to balance capturing long-range dependencies with computational efficiency, as convolutional neural networks focus on local features while transformers incur high computational costs. To address this tradeoff, we propose a parallel lattice a...

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主要な著者: Allen Patnaik, Arpit Gour, M. K. Bhuyan, Karl F. MacDorman, Sultan Alfarhood, Mejdl Safran
フォーマット: Artigo
言語:Inglês
出版事項: IEEE 2026-01-01
シリーズ:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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オンライン・アクセス:https://ieeexplore.ieee.org/document/11370168/
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