While lightweight ViT framework has made tremendous progress in image
super-resolution, its uni-dimensional self-attention modeling, as well as
homogeneous aggregation scheme, limit its effective receptive field (ERF) to
include more comprehensive interactions from both spatial and channel
dimensions. To tackle these drawbacks, this work proposes two enhanced
components under a new Omni-SR architecture. First, an Omni Self-Attention
(OSA) block is proposed based on dense interaction principle, which can
simultaneously model pixel-interaction from both spatial and channel
dimensions, mining the potential correlations across omni-axis (i.e., spatial
and channel). Coupling with mainstream window partitioning strategies, OSA can
achieve superior performance with compelling computational budgets. Second, a
multi-scale interaction scheme is proposed to mitigate sub-optimal ERF (i.e.,
premature saturation) in shallow models, which facilitates local propagation
and meso-/global-scale interactions, rendering an omni-scale aggregation
building block. Extensive experiments demonstrate that Omni-SR achieves
record-high performance on lightweight super-resolution benchmarks (e.g., 26.95
dB@Urban100 $\times 4$ with only 792K parameters). Our code is available at
https://github.com/Francis0625/Omni-SR.

提出了一种名为 Omni-SR 的新型架构，通过增强的 Omni Self-Attention 块和多尺度交互机制来改善轻量级 ViT 框架中 one-dimensional self-attention 建模的局限和同构聚合方案，并在图像超分辨率任务中获得最高记录性能。