The super-resolution reconstruction technology can improve the image quality and make the original image have richer details. Aiming at the problem that the existing super-resolution reconstruction algorithm have a single extraction feature, which is not conducive to the further extraction of image information, a super-resolution reconstruction algorithm based on multi-scale feature fusion is proposed. The multi-feature extraction module was used to obtain more shallow information, and densely connected structures were added to the network to enhance the propagation of features, reduced the calculation of related parameters, and alleviated the problem of gradient disappearance. Experiments were carried out on the Set5 and Set14 benchmark datasets, and further verified the effectiveness of the algorithm on the power inspection datasets. Compared with mainstream super-resolution reconstruction methods, the experimental results show that the image generated by this method has richer detailed information, which can effectively improve the image quality. The peak signal-to-noise ratio and structural similarity values have a certain improvement compared with other mainstream algorithms.