Video super resolution based on deformable 3D convolutional group fusion

Video super resolution aims to generate high resolution video sequences from corresponding low resolution video sequences. Aiming at improving the insufficient utilization of temporal and spatial information of video sequences in current video super resolution methods, we proposed a new network based on deformable 3D convolutional group fusion. Input sequences were divided into groups according to different frame rates, which can effectively integrate time information in a hierarchical manner. The deformable 3D convolution was used for integration points within the good group of characteristics to keep the spatial and temporal correlation of video sequences. The introduction of time attention mechanism and group integration module provided supplementary information fusion for each group, to restore the missing details in the video sequence and generate high resolution video frames. Experimental results on Vid4 standard video data set show that The PSNR and SSIM of the generated high-resolution video frames are 27.39 and 0.8266, respectively. The network presented in this study has a good effect on the processing of motion video and has achieved better performance than current advanced methods.