| Citation: | CHENG D Q，WANG P J，DONG Y Q，et al. Image super-resolution reconstruction network based on multi-scale spatial attention guidance[J]. Journal of Beijing University of Aeronautics and Astronautics，2025，51（7）：2185-2195 （in Chinese） doi: 10.13700/j.bh.1001-5965.2023.0547
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Aiming at the problem that the attention-mechanism-based image super-resolution reconstruction network ignores the heterogeneity of attentional features and treats features at different levels uniformly by directly incorporating the attention mechanism into the network model. This study designs a novel multi-scale spatial attention guidance network, namely SAGN, which makes the following key contributions. Firstly, an enhanced feature extraction residual block (ERB) is proposed to enhance the representation capacity of local information. Secondly, to record spatial attention features at various scales, a multi-scale spatial attention (MSA) module is incorporated. Lastly, an attention-guided module (AGM) is introduced to assign individualized weights to different features, facilitating effective fusion of contextual global features and suppression of redundant information. On four benchmark datasets, extensive experimental results show that SAGN outperforms standard attention structures in terms of both subjective visual perception and objective evaluation criteria. Notably, SAGN achieves an average 0.05 dB higher than that of the suboptimal model in peak signal-to-noise ratio (PSNR) for 4 times reconstruction results, further underscoring its efficacy in recovering image geometric structures and fine details.
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