Omni Aggregation Networks for Lightweight Image Super-Resolution
Hang Wang, Xuanhong Chen, Bingbing Ni, Yutian Liu, Jinfan Liu

TL;DR
This paper introduces Omni-SR, a lightweight image super-resolution architecture with omni-directional self-attention and multi-scale interaction, significantly improving performance while maintaining efficiency.
Contribution
The work proposes the Omni Self-Attention block and omni-scale aggregation scheme, enabling comprehensive spatial and channel interactions in lightweight super-resolution models.
Findings
Achieves record-high performance on lightweight SR benchmarks
Omni-SR outperforms existing models with fewer parameters
Demonstrates effective modeling of spatial and channel correlations
Abstract
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.,…
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Taxonomy
TopicsAdvanced Image Processing Techniques · Advanced Vision and Imaging · Image and Video Quality Assessment
