  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.
