Super-resolution reconstruction method based on deep learning local and non-local information

A technology of super-resolution reconstruction and deep learning, applied in the field of super-resolution reconstruction based on local and non-local information of deep learning, image super-resolution reconstruction

Active Publication Date: 2021-02-02
SICHUAN UNIV
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  • Application Information

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Problems solved by technology

However, most of the existing super-resolution reconstruction methods based on deep learning are local convo

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  • Super-resolution reconstruction method based on deep learning local and non-local information
  • Super-resolution reconstruction method based on deep learning local and non-local information
  • Super-resolution reconstruction method based on deep learning local and non-local information

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Embodiment Construction

[0010] The present invention will be further described below in conjunction with accompanying drawing:

[0011] figure 1 Among them, the super-resolution reconstruction method based on deep learning local and non-local information can be divided into the following steps:

[0012] (1) Build a super-resolution convolutional neural network model based on deep learning local and non-local information, including two modules: local network and non-local enhancement network;

[0013] (2) Using the convolutional neural network in step 1, train super-resolution models with different amplification factors;

[0014] (3) Based on the trained super-resolution reconstruction model, the low-resolution image is used as input to obtain the final super-resolution reconstruction image.

[0015] Specifically, in step (1), the super-resolution convolutional neural network model based on local and non-local information of deep learning is built as figure 1 As shown, it includes two modules: loca...

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Abstract

The invention discloses a super-resolution reconstruction method based on deep learning local and non-local information. The method mainly comprises the following steps: establishing a super-resolution convolutional neural network model based on deep learning local and non-local information, wherein the super-resolution convolutional neural network model comprises a local network module and a non-local enhancement network module; respectively training super-resolution models of different amplification factors by using the convolutional neural network built in the previous step; and taking thetrained super-resolution reconstruction model as a basis, taking the low-resolution image as input, and obtaining a final super-resolution reconstruction image. According to the method provided by theinvention, effective information of a wider area of the image can be mined by utilizing the non-local enhancement network, so the super-resolution reconstruction can be effectively carried out on thelow-resolution image, a good subjective and objective effect can be obtained, and the method is an effective low-resolution image restoration method.

Description

technical field [0001] The invention relates to image super-resolution reconstruction technology, in particular to a super-resolution reconstruction method based on deep learning of local and non-local information, belonging to the field of digital image processing. Background technique [0002] Super-resolution reconstruction technology is widely used in real life because it can improve the resolution of images, such as from imaging in the security field to medical imaging. Therefore, in the field of image processing, super-resolution reconstruction technology has been widely used by many researchers favored and studied in depth. There are two main methods to improve image resolution: one is to obtain high-resolution images by improving hardware equipment conditions; the other is to increase image resolution by software. The method realized by improving hardware conditions often has relatively high cost, cannot increase the resolution of captured images, and has strong lim...

Claims

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Application Information

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IPC IPC(8): G06T3/40G06N3/04G06N3/08
CPCG06T3/4053G06N3/08G06N3/045
Inventor 何小海占文枢陈正鑫任超熊淑华王正勇滕奇志
Owner SICHUAN UNIV
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