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Video super-resolution enhancement based on 3D spatio-temporal fusion model

In the field of video super-resolution, multiple frames provide more scene information, not only in intra-frame spatial dependence, but also inter-frame temporal dependence (for example motion, brightness, and color changes). Therefore, existing work mainly focuses on making better use of spatio-temporal dependence, including explicit motion compensation (for example based on optical flow, based on learning) and cyclic methods. After years of research by predecessors, video super-resolution has achieved good results, but there are still some difficulties and shortcomings that need to be resolved. Generally speaking, SR algorithm series using deep learning technology are different from each other in the following main aspects: different types of network architectures, different types of loss functions, and different types of learning strategies. In this paper, the spatio-temporal fusion module is improved on the basis of a two-stage 3D convolutional network. The adjacent frame and the current frame are merged in space and time, and the generated residual block is added to the feature map. The two-stage 3D convolution is applied to the residual block in the backbone network DenseNet to better process the pre-deblurring module.