Open AccessResearch on Super-Resolution Reconstruction Algorithm of Image Based on Generative Adversarial Network
Author(s) -
Rongzhao Jia,
Xiaohong Wang
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 - 10.1088/1742-6596/1944/1/012014
Subject(s) - computer science , artificial intelligence , image (mathematics) , convolution (computer science) , set (abstract data type) , algorithm , computer vision , generative adversarial network , iterative reconstruction , data set , image resolution , resolution (logic) , field (mathematics) , image restoration , pattern recognition (psychology) , image processing , mathematics , artificial neural network , pure mathematics , programming language
Image super-resolution is to use a series of algorithms to improve the original image resolution. The process of obtaining high-resolution image through some low-resolution images is image super-resolution reconstruction technology. There are two main research fields in super-resolution reconstruction, one is to restore the real details of the image, and the other is to not require too much detail, and only pay attention to the overall visual effect of the image. In this paper, an improved super-resolution reconstruction algorithm based on generative adversarial network is proposed. The network model and loss function are improved and optimized. The auxiliary VGG-19 network is used to extract the image features, and the extended convolution is used to expand the receptive field, which makes the image have a better reconstruction effect. Using DIV2k data set as training set and set5, set14, bsd100 data sets as test set, a series of experimental analysis is to prove the feasibility of the procedure method. Compared with the existing mainstream models, the perceptual effect of the image has been improved.