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Recursive residual attention network-based image super-resolution reconstruction method

A technology of super-resolution reconstruction and image resolution, applied in image data processing, graphics and image conversion, instruments, etc., can solve problems such as reducing network parameters, and achieve the effect of solving large model parameters, good feature attributes, and improving accuracy

Active Publication Date: 2018-11-06
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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  • Recursive residual attention network-based image super-resolution reconstruction method
  • Recursive residual attention network-based image super-resolution reconstruction method
  • Recursive 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 a recursive residual attention network-based image super-resolution reconstruction method. The method is characterized by comprising the following steps of: 1) preprocessing data; 2) establishing a reconstruction model; 3) extracting features of a first residual attention module of a residual attention network branch; 4) extracting features of a first recursive module of arecursive network branch; 5) fusing the features; and 6) reconstructing an image. According to the method, noises caused by preprocessing can be solved, more high-frequency information can be obtainedto enrich the image details, the network parameters can be decreased, new parameters are not increased while the layers are increased; and the precision of super-resolution reconstruction can be improved.

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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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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