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Super-resolution image reconstruction method and device based on depth learning

A technology for high-resolution images and low-resolution images, which is applied in the field of super-resolution image reconstruction based on deep learning, and can solve problems such as inconsistent super-resolution enhancement effects

Active Publication Date: 2019-01-04
GUANGZHOU SHIYUAN ELECTRONICS CO LTD
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Problems solved by technology

[0004] However, in the existing learning methods, the enhancement effect of super-resolution is inconsistent for images of different scales

Method used

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  • Super-resolution image reconstruction method and device based on depth learning
  • Super-resolution image reconstruction method and device based on depth learning
  • Super-resolution image reconstruction method and device based on depth learning

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Embodiment Construction

[0068] Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numerals in different drawings refer to the same or similar elements unless otherwise indicated.

[0069] figure 1 It is a flow chart of the deep learning-based super-resolution image reconstruction method provided by Embodiment 1 of the present invention. This embodiment can be executed by a terminal device with an image processing function, such as a personal computer, a smart phone, a tablet computer, a personal digital assistant (personal digital assistant, PDA), laptop, TV, etc. Such as figure 1 As shown, the method of the present embodiment includes the following steps:

[0070] Step S101, according to the image set and the target magnification, establish a training set corresponding to high-resolution images and low-resolution images.

[0071] The image s...

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Abstract

The invention provides a super-resolution image reconstruction method and device based on depth learning, including the following steps: according to the image set and the magnification of the target,a training set corresponding to the high-resolution image and the low-resolution image is established, based on the training set and the pre-constructed multi-scale network model, network training isconducted, so that the model parameter are obtained, wherein the multi-scale network model comprises a plurality of feature extraction networks and a composite network, multiple feature extraction networks are used to extract features from images, and combined networks are used to combine multiple sets of features extracted from multiple feature extraction networks; using the trained multi-scalenetwork model, the input low-resolution images are reconstructed to obtain high-resolution images. A better reconstruction effect can be obtained by extracting features from images through multiple feature extraction networks with different network depths and combining multiple features.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to a super-resolution image reconstruction method and device based on deep learning. Background technique [0002] Super-resolution (Super-Resolution) is to improve the resolution of the original image through hardware or software. The process of obtaining a high-resolution image through a series of low-resolution images is super-resolution reconstruction. Super-resolution has broad application prospects in the fields of video compression and transmission, medical image-aided diagnosis, security monitoring, and satellite imaging. [0003] Super-resolution mainly has the following two evaluation criteria: (1) the reconstruction effect of the image, the goal of reconstruction is to restore the high-frequency information of the image, improve the quality of the image, and improve the visual effect of the reconstructed image as much as possible; (2) the image quality T...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T3/40G06K9/46G06N3/04G06N3/08
CPCG06N3/08G06T3/4053G06V10/462G06N3/045
Inventor 邹超洋
Owner GUANGZHOU SHIYUAN ELECTRONICS CO LTD
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