Abstract: To address the problem of the local effect of the output features of cascaded residual blocks in single image super-resolution algorithms,a residual feature aggregation network combined with attention mechanism is proposed.The network aggregates the features of different levels output by each residual block through skip connections to the end of the residual group,achieves sufficient feature extraction and reuse,expands the receptive field of the network and enhances the expression ability of features.Meanwhile,to improve the spatial correlation of feature information,an enhanced spatial attention mechanism is introduced to improve the performance of the residual blocks.Extensive experiments demonstrate that the proposed model achieves good super-resolution performance.Compared with state-of-the-art methods such as RCAN,SAN,and HAN,the proposed method demonstrates significant effectiveness and advancement in the task of ×4 super-resolution.On five benchmark datasets,our method achieves an average improvement of 0.07dB,0.06dB,and 0.006dB in peak signal-to-noise ratio,as well as an average improvement of 0.001 2,0.001 1,and 0.0008 in structural similarity index.The reconstructed images exhibit a notable increase in quality,with more abundant details.These results verifies he efficacy and advancement of the proposed method.

Key words: Image super-resolution reconstruction, Deep learning, Attention mechanismsl, Feature aggregation, Convolutional neural networks