Super Resolution Reconstruction Algorithm of UAV Image Based on Residual Neural Network

With the development of convolutional neural network, video super-resolution algorithm has achieved remarkable success. Because the dependence between frames is complex, traditional methods lack the ability to model the complex dependence, and it is difficult to estimate and compensate the motion accurately in the process of video super-resolution reconstruction. Therefore, a reconstruction network based on optical flow residuals is proposed. In low resolution space, the dense residual network is used to obtain the complementary information of adjacent video frames, and then the optical flow of high-resolution video frames is predicted through the pyramid structure, and then the low resolution video frames are transformed into high-resolution video frames through the sub-pixel convolution layer, The high-resolution video frame is compensated with the predicted high-resolution optical flow. Finally, it is input into the super-resolution fusion network to get better effect. A new loss function training network is proposed to better constrain the network. Experimental results on public data sets show that the reconstruction effect is improved in PSNR, structural similarity and subjective visual effect.