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.

我们提出了一种新颖的可学习协同注意力（LCoA）方法，通过引入归纳偏差来解决非局部建模中的高计算复杂性和内存消耗问题，并将其集成到深度可学习协同注意力网络（LCoAN）中，与其他先进的超分辨率方法相比，在推理时间、内存消耗和重建质量等方面取得了有竞争力的性能。