A Recurrent Residual Attention Network Based Image Super-Resolution Reconstruction Method

A technology of super-resolution reconstruction and image resolution, applied in graphics and image conversion, image data processing, instruments, etc., can solve problems such as reducing network parameters, and achieve the effect of reducing network parameters, solving noise, and enriching image details

Active Publication Date: 2022-04-12
GUILIN UNIV OF ELECTRONIC TECH
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Problems solved by technology

This method can solve the noise caused by the preprocessing operation, and obtain more high-frequency information to enrich image details, while reducing network parameters, increasing the number of layers without adding new parameters, and improving super-resolution reconstruction the accuracy of

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  • A Recurrent Residual Attention Network Based Image Super-Resolution Reconstruction Method
  • A Recurrent Residual Attention Network Based Image Super-Resolution Reconstruction Method
  • A Recurrent Residual Attention Network Based Image Super-Resolution Reconstruction Method

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Embodiment

[0034] refer to figure 1 , an image super-resolution reconstruction method based on a recursive residual attention network, comprising the following steps:

[0035] 1) Data preprocessing: perform bicubic interpolation on the original input image, enlarge the resolution of the original input image to the same size as the desired image resolution, and generate a multi-scale training set according to different interpolation magnifications;

[0036] 2) Establish reconstruction model: such as figure 2As shown, the reconstruction model includes a residual attention network branch and a recursive network branch. The residual attention network branch is composed of a series of residual attention modules with the same structure, and the recurrent network branch is also composed of a Composed of recursive modules in series, the residual attention module corresponds to the recursive module one by one, the output of the residual attention module is connected to the output of the recursi...

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Abstract

The invention discloses an image super-resolution reconstruction method based on a recursive residual attention network, which is characterized in that it comprises the following steps: 1) data preprocessing; 2) establishment of a reconstruction model; 3) residual attention network branch Feature extraction of the first residual attention module; 4) feature extraction of the first recursive module of the recurrent network branch; 5) feature fusion; 6) image reconstruction. This method can solve the noise caused by the preprocessing operation, and obtain more high-frequency information to enrich image details, while reducing network parameters, increasing the number of layers without adding new parameters, and improving super-resolution reconstruction accuracy.

Description

technical field [0001] The invention relates to the technical field of intelligent image processing, in particular to an image super-resolution reconstruction method based on a recursive residual attention network. Background technique [0002] Single image super-resolution (Single Image Super-Resolution, referred to as SISR) reconstruction is a classic hot issue in the field of computer vision, aiming to reconstruct a high-resolution image from a low-resolution (Low-Resolution, referred to as LR) image (High-Resolution, HR for short) image. Single image super-resolution can break through the limitation of hardware equipment, improve image resolution, and is widely used in satellite remote sensing images, medical images, security supervision and other fields that require high-definition image sources. [0003] In traditional methods, reconstruction is performed from several instances of low-resolution images in pairs. While super-resolution reconstruction based on deep lea...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T3/40
CPCG06T3/4053
Inventor 林乐平梁婷欧阳宁莫建文袁华首照宇张彤陈利霞
Owner GUILIN UNIV OF ELECTRONIC TECH
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