An image super-resolution reconstruction method based on multi-scale generation countermeasure network

A super-resolution reconstruction, multi-scale technology, applied in image analysis, biological neural network model, image enhancement and other directions, can solve the problems of poor high-frequency information extraction, poor display of high-frequency information details, etc., to achieve good display effect Effect

Active Publication Date: 2019-01-04
XUZHOU UNIV OF TECH
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[0005] However, the above-mentioned methods are poor in extracting high-frequency information from low-resolution input images, so that the

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  • An image super-resolution reconstruction method based on multi-scale generation countermeasure network
  • An image super-resolution reconstruction method based on multi-scale generation countermeasure network
  • An image super-resolution reconstruction method based on multi-scale generation countermeasure network

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[0038] The present invention will be further explained below.

[0039] As shown in the figure, an image super-resolution reconstruction method based on multi-scale generative confrontation network, the specific steps are:

[0040] (1) Build a multi-scale generative confrontation network structure and complete the pre-training of its generator:

[0041] A. The generator of the multi-scale generative confrontation network is composed of multiple single-scale feature extraction sub-networks, and the single-scale feature extraction sub-network is composed of multiple SENet (compressed activation) modules;

[0042] B. Determine the target loss function, complete the pre-training process of the generator, and improve the convergence speed of the multi-scale generation confrontation network;

[0043] (2) Multi-scale Generative Adversarial Network for image super-resolution reconstruction:

[0044] Ⅰ. Input the pre-reconstructed LR image to the upsampling layer of the first single-scale feature ...

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Abstract

The invention discloses an image super-resolution reconstruction method based on a multi-scale generation antagonistic network, which adopts a multi-scale SENet module as a generator and finally formsa multi-scale generation antagonistic network. Then the target loss function is determined and the pre-training process of the generator is completed so as to improve the convergence speed of the multi-scale generation countermeasure network. The feature extraction of high-frequency information of LR input image is realized by the multi-scale generation countermeasure network, then the HR image is reconstructed with the result of bi-cubic interpolation; the discriminator discriminates the true and false of the reconstructed image, and weighs the loss of mean square error and the loss of antagonism as the final adjustment objective function; after adjusting the output HR image by the objective function, the whole image reconstruction process is completed. The invention can extract more high-frequency information details of the LR input image, thereby enabling the HR image with better display effect to be generated after the image super-resolution reconstruction.

Description

technical field [0001] The invention relates to an image super-resolution reconstruction method based on a multi-scale generation confrontation network. Background technique [0002] The image super-resolution (Single image super-resolution, SISR) reconstruction task refers to learning more image details from a low-resolution (Low-resolution, LR) image input to generate a high-resolution (High-resolution, HR) image. Since HR images can learn important details of the original images, SISR technology is widely used, including video surveillance, medical diagnosis, face recognition, etc. Traditional image super-resolution methods are mainly interpolation algorithms, such as bicubic interpolation and nearest neighbor interpolation, which estimate the value of unknown pixels in SR images by using fixed or structure-adapted kernel functions. Although the interpolation algorithm is more efficient, the result obtained is blurred and the edge details are smoother. [0003] In recen...

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Application Information

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IPC IPC(8): G06T3/40
CPCG06T3/4007G06T3/4076G06T2207/20081G06T2207/20084G06N3/082G06N3/047G06N3/045
Inventor 姜代红黄忠东鞠训光戴磊孙天凯刘其开
Owner XUZHOU UNIV OF TECH
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