Image super-resolution method based on deep threshold convolutional neural network

A convolutional neural network and super-resolution technology, applied in graphics and image conversion, image data processing, instruments, etc., can solve the problems of model learning effect decline, short time, etc., achieve fast speed, reduce gradient disappearance, and fast high-resolution images Effect

Active Publication Date: 2019-08-13
XIDIAN UNIV
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Problems solved by technology

In the application process, since no additional learning parameters are needed, the time is short; but the disadvantage of this method is that the learning effect of the model will decrease when the number of layers of the network is deepened.

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  • Image super-resolution method based on deep threshold convolutional neural network
  • Image super-resolution method based on deep threshold convolutional neural network
  • Image super-resolution method based on deep threshold convolutional neural network

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Embodiment Construction

[0029] The embodiments and effects of the present invention will be described in further detail below in conjunction with the accompanying drawings.

[0030] refer to figure 1 , the realization steps of the present invention are as follows.

[0031] Step 1: Obtain pairs of low-resolution and high-resolution image data.

[0032] 1.1) Obtain low-resolution images:

[0033] The original image is first down-sampled, and then the down-sampled image is restored to its original size by bilinear cubic interpolation, and the obtained picture is a low-resolution image;

[0034] The bilinear cubic interpolation is performed by the following formula:

[0035] f(i+u,j+v)=ABC

[0036] Among them, u represents the horizontal interpolation position, v represents the vertical interpolation position, i is the abscissa of the current pixel, j is the ordinate of the current pixel, f(i+u,j+v) indicates that the image is in (i+ The interpolated pixel value at u,j+v); A is the horizontal factor...

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Abstract

The invention discloses an image super-resolution method based on a deep threshold convolutional neural network, which is mainly used to solve the problem that the super-resolution effect of an image is reduced when the network is deepened in the prior art. The method comprises the following steps: (1) acquiring paired low-resolution and high-resolution image data as training data; (2), defining a threshold convolution layer to replace the existing convolution layer, and building an end-to-end deep threshold convolutional neural network; (3) inputting the training data to the deep threshold convolutional neural network to train the network through an Adam optimization method; and (4) using the trained deep threshold convolutional neural network to carry out image super-resolution. The gradient attenuation problem of the deep neural network is reduced. The application of the deep network in image super resolution is realized. The effect of image super-resolution is enhanced. The speed of image super-resolution is increased. The image super-resolution method can be used in fields such as satellite remote sensing, medicine, traffic monitoring and video compression.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to an image super-resolution method, which can be used in the fields of satellite remote sensing, medicine, traffic monitoring, video compression and the like. Background technique [0002] Image super-resolution refers to a technique that learns to restore a high-resolution image from a low-resolution image. Compared with low-resolution images, high-resolution images can express more detailed information, and its ability to express details is stronger. Therefore, image super-resolution has great applications in many fields, such as satellite remote sensing, medical fields, The field of traffic monitoring, and the field of video compression, etc. [0003] So far, there are three main categories of image super-resolution methods: interpolation-based, reconstruction-based, and learning-based methods. The learning-based method has made great progress in recent years and has ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T3/40
CPCG06T3/4053
Inventor 陈渤刘明贵
Owner XIDIAN UNIV
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