CATANet: Efficient Content-Aware Token Aggregation for Lightweight Image Super-Resolution
This addresses the problem of high computational cost in lightweight image super-resolution for applications requiring efficient processing, though it is incremental as it builds on existing cluster-based methods.
The paper tackles the computational complexity of transformer-based image super-resolution by proposing CATANet, which aggregates content-similar tokens to capture long-range dependencies efficiently, achieving a maximum PSNR improvement of 0.33dB and nearly double the inference speed compared to SPIN.
Transformer-based methods have demonstrated impressive performance in low-level visual tasks such as Image Super-Resolution (SR). However, its computational complexity grows quadratically with the spatial resolution. A series of works attempt to alleviate this problem by dividing Low-Resolution images into local windows, axial stripes, or dilated windows. SR typically leverages the redundancy of images for reconstruction, and this redundancy appears not only in local regions but also in long-range regions. However, these methods limit attention computation to content-agnostic local regions, limiting directly the ability of attention to capture long-range dependency. To address these issues, we propose a lightweight Content-Aware Token Aggregation Network (CATANet). Specifically, we propose an efficient Content-Aware Token Aggregation module for aggregating long-range content-similar tokens, which shares token centers across all image tokens and updates them only during the training phase. Then we utilize intra-group self-attention to enable long-range information interaction. Moreover, we design an inter-group cross-attention to further enhance global information interaction. The experimental results show that, compared with the state-of-the-art cluster-based method SPIN, our method achieves superior performance, with a maximum PSNR improvement of 0.33dB and nearly double the inference speed.