Open AccessLogo Based on Improved Generative Countermeasure Network Image Super Resolution Reconstruction Method
Author(s) -
Pengcheng Luo,
HU Geng-sheng,
Zhiping Tan
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1881/4/042040
Subject(s) - artificial intelligence , computer science , aliasing , computer vision , noise (video) , iterative reconstruction , noise reduction , image (mathematics) , image restoration , sampling (signal processing) , image processing , filter (signal processing) , undersampling
In order to solve the problem that the existing learning based super-resolution reconstruction methods rely too much on the degenerate model while the degradation model is unknown, which leads to noise and edge jagged in the reconstructed image, an image super-resolution reconstruction method based on improved generative countermeasure network is proposed. The generator of the network consists of up sampling module, denoising module and anti-aliasing module Firstly, the input image is sampled by the up sampling module to generate the initial super-resolution image; then, the denoising module and anti-aliasing module are used to reconstruct the clear super-resolution image; in order to reconstruct a better logo image, a joint training loss is introduced, including content loss and resistance loss, and the content loss includes perception loss and edge loss Lost. The experimental results show that, compared with the current super-resolution reconstruction method based on generative countermeasure network (SRGAN), the peak signal-to-noise ratio (PSNR) of the reconstructed image is improved to 0.2675dB and the structural similarity is improved to 0.035, which can effectively improve the quality of logo image reconstruction.