Efficient Learnable Collaborative Attention for Single Image Super-Resolution
<|reference_start|>Efficient Learnable Collaborative Attention for Single Image Super-Resolution: Non-Local Attention (NLA) is a powerful technique for capturing long-range feature correlations in deep single image super-resolution (SR). However, NLA suffers from high computational complexity and memory consumption, as it requires aggregating all non-local feature information for each query response and recalculating the similarity weight distribution for different abstraction levels of features. To address these challenges, we propose a novel Learnable Collaborative Attention (LCoA) that introduces inductive bias into non-local modeling. Our LCoA consists of two components: Learnable Sparse Pattern (LSP) and Collaborative Attention (CoA). LSP uses the k-means clustering algorithm to dynamically adjust the sparse attention pattern of deep features, which reduces the number of non-local modeling rounds compared with existing sparse solutions. CoA leverages the sparse attention pattern and weights learned by LSP, and co-optimizes the similarity matrix across different abstraction levels, which avoids redundant similarity matrix calculations. The experimental results show that our LCoA can reduce the non-local modeling time by about 83% in the inference stage. In addition, we integrate our LCoA into a deep Learnable Collaborative Attention Network (LCoAN), which achieves competitive performance in terms of inference time, memory consumption, and reconstruction quality compared with other state-of-the-art SR methods.<|reference_end|>
@article{zheng2024efficient,
    title={Efficient Learnable Collaborative Attention for Single Image
  Super-Resolution},
    author={Yigang Zhao Chaowei Zheng, Jiannan Su, GuangyongChen and MinGan},
    journal={arXiv preprint arXiv:2404.04922},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.04922},
    primaryClass={cs.CV cs.AI}
}