Efficient Learnable Collaborative Attention for Single Image Super-Resolution

• URL: http://arxiv.org/abs/2404.04922v1
• Date: Sun, 7 Apr 2024 11:25:04 GMT
• Title: Efficient Learnable Collaborative Attention for Single Image Super-Resolution
• Authors: Yigang Zhao Chaowei Zheng, Jiannan Su, GuangyongChen, MinGan,
• Abstract summary: Non-Local Attention (NLA) is a powerful technique for capturing long-range feature correlations in deep single image super-resolution (SR) We propose a novel Learnable Collaborative Attention (LCoA) that introduces inductive bias into non-local modeling. Our LCoA can reduce the non-local modeling time by about 83% in the inference stage.
• Score: 18.955369476815136
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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