Single-image super-resolution method and system based on simplified ESRGAN

Abstract

The invention relates to a single-image super-resolution method based on a simplified ESRGAN, and the method comprises the following steps: S1, obtaining a to-be-processed low-resolution image, and carrying out the preprocessing of the to-be-processed low-resolution image; s2, according to the preprocessed image, generating a super-resolution image through a generator module in the improved single-image super-resolution generative adversarial network, if the model is in a training stage, carrying out the step S3, and otherwise, carrying out the step S4; s3, constructing a discriminator, usingthe discriminator to judge whether the super-resolution image is a real high-resolution image or not, performing back propagation according to a result obtained by the discriminator, optimizing the generator, and performing the step S2 again; and S4, carrying out edge restoration processing on the obtained super-resolution image to obtain a final super-resolution image. According to the method, the problem of edge restoration after image amplification is solved, the edge sawtooth effect and the blocking effect are removed, the image is smoother, and therefore single-image super-resolution reconstruction is well achieved.

Description

Technical field[0001]The invention relates to the field of image super-resolution, in particular to a single-image super-resolution method and system based on simplified ESRGAN.Background technique[0002]Image super-resolution reconstruction aims to study the generation of super-resolution (SR) images with better visual effects from low-resolution (LR) images. It is widely used in the fields of game profile resolution reshaping, medical and military, etc., for people Provide convenient and automated tools to improve image quality and utilization value.[0003]The current research on image super-resolution reconstruction is mainly divided into three categories:[0004]Super-resolution reconstruction based on interpolation. Image interpolation refers to the use of known gray values ​​of neighboring pixels to generate gray values ​​of unknown pixels, so that an image with higher resolution can be regenerated from the original image. There are many super-resolution image reconstruction metho...

AI Technical Summary

Problems solved by technology

[0007] In view of this, the object of the present invention is to provide a single image super-resolution method and system based on simplified ESRGAN, which converts low-resolution images into higher-resolution images High-rate images, and use bicubic interpolation for post-processing to solve the problem of edge repair after image enlargement, remove edge aliasing and block effects, and make the image smoother, so as to better achieve single-image super-resolution reconstruction

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