Multi-frame video super-resolution method fused with attention mechanism

A multi-frame super-resolution and super-resolution technology, applied in neural learning methods, image data processing, instruments, etc., can solve problems such as complex motion, achieve enhanced expression ability, improve super-resolution effect, and improve diversity Effect

Active Publication Date: 2020-06-09
SUN YAT SEN UNIV
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AI Technical Summary

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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  • Multi-frame video super-resolution method fused with attention mechanism
  • Multi-frame video super-resolution method fused with attention mechanism
  • 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 fused with an attention mechanism. The method comprises the following steps: collecting video data and training the video data by adopting a video enhancement technology to generate a training set and a test set; connecting the deformation convolution feature alignment module and the feature reconstruction module to form a multi-frame super-resolution network, and training the multi-frame super-resolution network by using the training set; adding a 3D convolution feature alignment module into the multi-frame super-resolution network, and training the multi-frame super-resolution network by adopting the training set; adding the feature fusion module into a multi-frame super-resolution network, and training the multi-frame super-resolution network by adopting the training set; finely adjusting the multi-frame super-resolution network by adopting the training set to generate a multi-frame super-resolution model; and testing the multi-frame super-resolution model by adopting the test set. According to the invention, the super-resolution effect can be effectively improved by analyzing 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 ...

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Application Information

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Patent Type & Authority Applications(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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