Abstract: Super-resolution is a widely used technology in many applications, such as video repair. Aiming at the insufficiency of the Fast Super-Resolution Convolutional Neural Networks (FSRCNN) method, an image super-resolution reconstruction method based on multi-scale joint network is proposed. Firstly, based on multi-scale structures, a feature sampling model is proposed to extract the features of Low-Resolution (LR) image. Secondly, the features are enhanced by feature fusion and sub-pixel convolutional layer. Finally, a joint loss function involving Mean Square Error (MSE) loss and Peak Signal to Noise Ratio (PSNR) loss is proposed to improve the optimization of the networks training. Comparison experiments were carried out on the sets of Set5, Set14, and BSD100. The experimental results show that the method has superiority against the state-of-the-art methods. Finally, the proposed method is applied to increase the resolutions of the television dramas “Journey to the West” and “The Dream of Red Mansion”, which achieves good visual effect.

Key words: multi-scale network, subpixel convolution, joint loss, super-resolution