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Time-dimension video super-resolution method based on deep learning

A deep learning and super-resolution technology, applied in the field of image processing, can solve the problems of unsatisfactory stability and accuracy of video images, and insufficient use of structural similarity, etc., to achieve the goals of reducing computational complexity, improving stability, and improving accuracy Effect

Inactive Publication Date: 2017-09-05
XIDIAN UNIV
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Problems solved by technology

The video image frame interpolation and reconstruction problem is an ill-conditioned inverse problem, which uses the time information of the video image and combines the spatial information of the video image to realize the video image frame interpolation reconstruction, but the algorithm does not make full use of the existing in the video image. The strong structural similarity between adjacent frames makes the stability and accuracy of the reconstructed video image difficult to meet the requirements of scientific research and large-scale practical applications

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  • Time-dimension video super-resolution method based on deep learning
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Embodiment Construction

[0028] Embodiments and effects of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0029] refer to figure 1 , the present invention is based on the time-dimensional video super-resolution method of deep learning, and its realization steps are as follows:

[0030] Step 1, get the color video image set S.

[0031] (1a) From a given database, select a color video image set S={S with a sample number of 464814 1 ,S 2 ,...,S i ,...,S 464814}, convert S to a grayscale video image set, that is, the original video image set X={X 1 ,X 2 ,...,X i ,...,X 464814},in, Represents the i-th original video image sample, 1≤i≤464814, M represents the size of the original video image block, M=576, L h Indicates the number of image blocks in each sample of the original video image set, L h = 6;

[0032] (1b) Use the downsampling matrix F to directly downsample the original video image set X to obtain the downsampled video ...

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Abstract

The invention discloses a time-dimension video super-resolution method based on deep learning, which is mainly aimed at solving the problems of the prior art that reconstructed video image insert frames are poor in stability and low in precision. The technical key of the method is that fitting of a nonlinear mapping relationship between an original video image and a down-sampling video image is conducted through neural network training. The method comprises the following steps: 1) obtaining an original video image set and a down-sampling video image set and taking the original video image set and the down-sampling video image set as training samples of a neural network; 2) constructing a neural network model, and training parameters of the neural network through the training samples; and 3) taking any given video as a test sample, and inputting the test sample into the trained neural network model, wherein an output result of the neural network is a reconstructed video image. The calculating complexity of reconstruction of video image insert frames is reduced, and the stability and the precision of the reconstructed video image insert frames are improved. The method can be used for scene interpolation and animation making and also for time-domain insert frames of low-frame-rate videos.

Description

technical field [0001] The invention belongs to the field of image processing, and in particular relates to a time-dimensional video super-resolution method, which can be used for scene interpolation, animation production, and time domain frame interpolation of low frame rate videos. Background technique [0002] The video image not only contains the spatial information of the observed target, but also contains the motion information of the observed target in time, which has the property of "integration of space and time". Since the video image can maintain the spatial information and time information reflecting the nature of the object together, it greatly improves the ability of human beings to recognize the objective world, and has been proved to have great potential in remote sensing, military affairs, agriculture, medicine, biochemistry and other fields. application value. [0003] The use of video imaging equipment to obtain precise video images is very costly, and is...

Claims

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Application Information

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IPC IPC(8): G06T3/40H04N19/587G06N3/08G06N3/04
CPCG06T3/4053G06N3/04G06N3/08H04N19/587
Inventor 董伟生巨丹石光明谢雪梅吴金建李甫
Owner XIDIAN UNIV
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