A Multi-frame Video Super-resolution Method Fused with Attention Mechanism

A multi-frame super-resolution and super-resolution technology, applied in neural learning methods, instruments, biological neural network models, etc., can solve problems such as complex motion, achieve enhanced expression ability, improve accuracy, and improve super-resolution effects Effect

Active Publication Date: 2020-12-22
SUN YAT SEN UNIV
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Problems solved by technology

The scene in the video has complex motion, and even often faces scene switching, which requires the super-resolution network to deal with this situation adaptively. The existing three methods have certain defects in the alignment between frames

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  • A Multi-frame Video Super-resolution Method Fused with Attention Mechanism
  • A Multi-frame Video Super-resolution Method Fused with Attention Mechanism
  • A Multi-frame Video Super-resolution Method Fused with Attention Mechanism

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Embodiment Construction

[0027] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0028] In the existing multi-frame video super-resolution task, it is necessary to use the information of adjacent frames to complement the current frame, and learn the mapping relationship from low-resolution images to high-resolution images from the features of multiple frames. The difficulty in obtaining good super-resolution results is the alignment of adjacent frame features and the effective use of aligned features. Previous methods extract multi-frame features through feature cascading, three-dimensional convolution or cyclic neural network, but these methods have their own shortcomings, and it is difficult to fully extract the effective features of each adjacent frame, resulting in the appearance of frame after super-resolution video. The phenomenon of dis...

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Abstract

The invention discloses a multi-frame video super-resolution method with fusion attention mechanism, comprising: collecting video data and using video enhancement technology to train the video data to generate a training set and a test set; connecting a deformation convolution feature alignment module and The feature reconstruction module is used to form a multi-frame super-resolution network, and the training set is used to train the multi-frame super-resolution network; the 3D convolution feature alignment module is added to the multi-frame super-resolution network, and the training set is used to train the multi-frame super-resolution network. The network is trained; the feature fusion module is added to the multi-frame super-resolution network, and the training set is used to train the multi-frame super-resolution network; the training set is used to fine-tune the multi-frame super-resolution network to generate a multi-frame super-resolution model ; The multi-frame super-resolution model is tested on the test set. The present invention can effectively improve the effect of super-resolution through the analysis of big data.

Description

technical field [0001] The invention relates to the technical field of big data analysis, in particular to a multi-frame video super-resolution method. Background technique [0002] Super-resolution technology is widely used in public security monitoring and identification, medical imaging, satellite remote sensing, virtual reality and other practical scenarios. Due to the development of visual media display technology, image and video data urgently need to have a better display or playback effect on existing high-definition displays, which also puts forward higher requirements for super-resolution technology. Compared with single-frame super-resolution, the video super-resolution task adds timing information. According to different ways of utilizing timing information, deep learning-based video super-resolution techniques can be roughly divided into methods based on multi-frame cascading, methods based on 3D convolution, and methods based on loop structures. [0003] The ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T3/40G06N3/04G06N3/08
CPCG06T3/4053G06T3/4046G06N3/08G06N3/045
Inventor 周凡苏卓林谋广陈小燕
Owner SUN YAT SEN UNIV
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