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Aiming at the problems that the convolutional neural networks neglect tocapture the inherent attributes of natural images and extract features only ina single scale in the field of image super-resolution reconstruction, a networkstructure based on attention mechanism and multi-scale feature fusion isproposed. By using the attention mechanism, the network can effectivelyintegrate the non-local information and second-order features of the image, soas to improve the feature expression ability of the network. At the same time,the convolution kernel of different scales is used to extract the multi-scaleinformation of the image, so as to preserve the complete informationcharacteristics at different scales. Experimental results show that theproposed method can achieve better performance over other representativesuper-resolution reconstruction algorithms in objective quantitative metricsand visual quality.

Image super-resolution reconstruction based on attention mechanism and feature fusion