Method and system for video super-resolution based on bidirectional circular convolutional network

Abstract

The invention discloses a video super-resolution method based on a two-way cyclic convolution network, comprising: establishing a two-way cyclic network, including a forward cyclic sub-network and a backward cyclic sub-network in chronological order, and each cyclic sub-network starts from the bottom Contains an input sequence layer, two hidden sequence layers and an output sequence layer, each sequence layer includes multiple states, corresponding to video frames at different times; use three convolution operations to connect these states, including feedforward Convolution, circular convolution and conditional convolution to obtain a bidirectional circular convolutional network; send the training video to the established bidirectional circular convolutional network, and use the stochastic gradient descent algorithm to minimize the predicted and true high-resolution The mean square error between the videos, thereby iteratively optimizing the weight of the network, and obtaining the final two-way circular convolution network; inputting the low-resolution video sequence to be processed to the final two-way circular convolution network model, and obtaining the corresponding super-resolution results.

Description

technical field [0001] The invention relates to the fields of computer vision and machine learning, in particular to a video super-resolution method and system based on a bidirectional circular convolution network. Background technique [0002] With the emergence of a large number of high-definition playback devices in recent years, how to convert low-resolution video to high-resolution video that is more suitable for playback, that is, super-resolution technology, has gradually become a hot research issue in the field of computer vision. [0003] The current super-resolution work can be roughly divided into two categories: 1) single-image super-resolution, which assumes that all video frames are independent of each other, and then performs super-resolution on each video frame independently. This method ignores a very important feature in video sequences, that is, the temporal dependence between video frames. 2) Multi-frame super-resolution, this method takes into account t...

AI Technical Summary

Problems solved by technology

[0004] However, in practical applications, this modeling of temporal dependencies usually requires high computational costs, which largely limits the application range of multi-frame super-resolution techniques.

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