A video super-resolution processing method and device

Abstract

The invention provides a video super-resolution processing method and device, and the method comprises the steps: obtaining each frame of image of a video, and inputting the image into a convolutionalneural network; Sequentially performing feature extraction, feature dimension reduction, nonlinear mapping and high-dimensional space mapping on the image through the convolutional neural network toobtain super-resolution features; Performing reconstruction according to the features obtained by feature extraction and the super-resolution features to obtain a super-resolution image; And finally,performing coding to form a super-resolution video code stream. The super-resolution processing of the video is realized through the convolutional neural network; Dimension reduction through features,nonlinear mapping and high-dimensional space mapping are carried out; the calculation complexity is reduced, the time complexity is reduced, the network learning difficulty is reduced and the complextexture of the output image is reserved by adopting the jump connection, so that the high reconstruction quality is realized while the real-time performance required by video processing is ensured, and the method has a very wide application prospect in the fields of video real-time transmission and compression, video restoration and the like.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to a video super-resolution processing method and device. Background technique [0002] At present, people's demand for video quality, especially video definition, is increasing day by day. Due to the limitations of current hardware devices, super-resolution of images and videos is usually implemented by software. Video super-resolution refers to converting a low-resolution video into a high-resolution video. This problem has attracted the attention of many researchers for a long time in the past, and many solutions have been proposed to solve this problem. model of the problem. [0003] Many image super-resolution methods in the prior art are dedicated to finding a mapping relationship from a low-resolution space to a high-resolution space, and the modeling methods include Markov random field, neighborhood embedding, and sparse coding. These methods have promote...

AI Technical Summary

Problems solved by technology

[0004] However, an obvious problem of the above method is that the time efficiency is relatively low, and the reconstruction quality cannot meet the higher fineness requirements, which greatly limits its performance in practical applications, especially it is difficult to apply to real-time video resolution processing

Images