In recent years, attention mechanisms have been exploited in single image super-resolution (SISR), achieving impressive reconstruction results. However, these advancements are still limited by the reliance on simple training strategies and network architectures designed for discrete up-sampling scales, which hinder the model's ability to effectively capture information across multiple scales. To address these limitations, we propose a novel framework, \textbf{C2D-ISR}, for optimizing attention-based image super-resolution models from both performance and complexity perspectives. Our approach is based on a two-stage training methodology and a hierarchical encoding mechanism. The new training methodology involves continuous-scale training for discrete scale models, enabling the learning of inter-scale correlations and multi-scale feature representation. In addition, we generalize the hierarchical encoding mechanism with existing attention-based network structures, which can achieve improved spatial feature fusion, cross-scale information aggregation, and more importantly, much faster inference. We have evaluated the C2D-ISR framework based on three efficient attention-based backbones, SwinIR-L, SRFormer-L and MambaIRv2-L, and demonstrated significant improvements over the other existing optimization framework, HiT, in terms of super-resolution performance (up to 0.2dB) and computational complexity reduction (up to 11%). The source code will be made publicly available atthis http URL.
View on arXiv@article{jiang2025_2503.13740,
title={ C2D-ISR: Optimizing Attention-based Image Super-resolution from Continuous to Discrete Scales },
author={ Yuxuan Jiang and Chengxi Zeng and Siyue Teng and Fan Zhang and Xiaoqing Zhu and Joel Sole and David Bull },
journal={arXiv preprint arXiv:2503.13740},
year={ 2025 }
}