To address the problem that image details are liable to be lost in the process of hyperspectral super-resolution, this study proposed a hyperspectral super-resolution algorithm that combines multi-receptive field features and spectral-spatial attention. By fully using the high- and low-frequency information in hyperspectral images, this algorithm reduces the loss of image details and improves the hyperspectral super-resolution effects. First, in the feature extraction stage, convolution with different sizes of convolutional kernels is used to obtain multi-scale receptive field features. This assists in extracting more high- and low-frequency information from low-resolution images, thus retaining the features of original images. Then, the acquired image features are enhanced by the spatial-spectral attention mechanism, and the reconstruction of spatial-dimension features is conducted using spectral-dimension information. Finally, the features of various groups are fused, and the checkerboard pattern is relieved by applying the pixel deconvolution layer. As a result, clear and high-resolution images can be produced. The proposed super-resolution algorithm that combines multi-receptive field features with spectral-spatial attention was applied to two public datasets Chikusei and Pavia Center Scene, achieving peak signal-to-noise ratios of 39.869 7 and 31.942 2, respectively and structural similarity of 0.937 6 and 0.878 6, respectively. Therefore, the super-resolution algorithm enjoys obvious performance advantages compared to the latest super-resolution algorithms. Overall, the algorithm proposed in this study integrates the advantages of the multi-receptive field feature extraction module and the spatial-spectral attention module and can significantly improve image details.
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