Video super-resolution method based on multi-frame attention mechanism progressive fusion

A technology of super-resolution and attention, applied in the direction of instruments, biological neural network models, graphics and image conversion, etc., can solve the problems of difficult estimation of accurate flow information, sensitivity of deformable convolution input, and affecting the quality of video reconstruction, etc. Achieve the effects of reducing fusion difficulty, accelerating convergence, and improving super-resolution efficiency
CN112991183AActive Publication Date: 2021-06-18SOUTH CHINA UNIV OF TECH
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Patent Information

Authority / Receiving Office
CN ยท China
Patent Type
Applications(China)
Current Assignee / Owner
SOUTH CHINA UNIV OF TECH
Publication Date
2021-06-18

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Abstract

The invention discloses a video super-resolution method based on a multi-frame attention mechanism progressive fusion. The method comprises the following steps: firstly, performing frame extraction on a video data set to generate a training set; then connecting a multi-frame attention mechanism progressive fusion module, a feature extraction module and a reconstruction module to construct a video super-division network, and then utilizing a low-redundancy training strategy to train the network on a training set, that is, only learning a target frame, and only using front and back frames as auxiliary information instead of the target frame for training to greatly improve the learning efficiency; and finally, reconstructing a to-be-amplified video by using the trained video super-division model to finally obtain a high-resolution video. The method can make full use of the information of the front and back frames to help the reconstruction of the target frame, and effectively improves the video super-resolution effect.
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Description

technical field

[0001] The present invention relates to the field of image super-resolution (SISR) technology and video super-resolution (VSR) technology based on deep learning, in particular to a video super-resolution method based on progressive fusion of multi-frame attention mechanism. Background technique

[0002] The image super-resolution (SISR) technology based on deep learning mainly uses convolutional neural network (CNN) as the learning model, and learns high-frequency information such as missing texture details of low-resolution images through a large amount of data, and realizes low-resolution images to high-resolution images. Resolution image end-to-end conversion. Compared with the traditional interpolation method, the deep learning method shows great advantages, and has achieved significant improvement in PSNR, SSIM and other effect evaluation indicators. In recent years, a large number of excellent image super-resolution based on deep learning have emerged. ...

Claims

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