The invention belongs to the technical field of
computer vision, and particularly provides a video super-resolution method based on a
convolutional neural network and
mixed resolution. The method comprises the following steps of firstly, prediorically revsering a part of frame in a
video sequence to serve as high-resolution frame data, subjecting other frames to degradation
processing to serve aslow-resolution image frames, and combinding the two frames are to form a
hybrid resolution video; secondly, training a degradation network based on the
convolutional neural network to extract featureinformation of a degradation factor, and obtaining a degradation feature map of the low-resolution
image frame by using the trained model; and then, taking the low-resolution
image frame and the high-resolution frame and degradation feature map related to the low-resolution
image frame as input data, performing training based on a
convolutional neural network to obtain a super-resolution
network model, and obtaining an output high-resolution video. The convolutional neural network and the
hybrid resolution model are combined, and
image texture details and degradation factors can be analyzed ina targeted manner, so that the super-resolution accuracy is improved.