Image super-resolution reconstruction based on multi-scale two-stage network

Aiming at the problems of the insufficient feature information extraction and the blurring of the reconstructed image details in current image super-resolution reconstruction algorithm, a multi-scale two-stage network was proposed to realize image super-resolution reconstruction. First of all, considering the phenomenon of insufficient feature information extraction in single-scale convolution layer, a network model was designed based on the general framework of multi-scale convolution layer.Secondly, considering the effect of the reconstructed image, the whole network was divided into two stages: the first stage was to extract and reconstruct the feature information according to the input low-resolution image, and the second stage was to further refine the features of the reconstructed image, so as to improve the visual effect of the reconstructed image. Jump connection and attention module were also introduced in the overall network to enhance the effective transmission of feature information. Finally, the data sets Set5, Set14, Urban100, BSDS100 and Manga109 were used as the test sets of the experiment, and the peak signal-to-noise ratio and the structural similarity were used as the evaluation indicators of image quality. The experiment shows that the values of both are improved and the visual effect of reconstructed image is good. Therefore, the algorithm has achieved good results in both objective evaluation and subjective vision.