Open AccessResearch on image super-resolution based on attention mechanism and multi-scale
Author(s) -
Xin Ren,
Xingzhen Li
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 - 10.1088/1742-6596/1792/1/012025
Subject(s) - discriminator , computer science , generator (circuit theory) , image (mathematics) , benchmark (surveying) , artificial intelligence , scale (ratio) , dual (grammatical number) , basis (linear algebra) , pattern recognition (psychology) , residual , feature (linguistics) , field (mathematics) , mechanism (biology) , data mining , computer vision , algorithm , mathematics , power (physics) , geography , art , telecommunications , linguistics , physics , geometry , literature , geodesy , philosophy , quantum mechanics , detector , pure mathematics , epistemology
In order to solve the problem of the single feature scale of the generated image in the SISR field and the lack of texture information, a parallel generation confrontation network structure based on the attention mechanism and multi-scale is proposed on the basis of SRGAN, which adopts a dual generator and discriminator combined with attention module model. Train the network to learn multi-scale features, and integrate high-frequency information of different scales in the residual network. The experimental results on Set5, Set14, and BSD100 benchmark data sets prove that the algorithm has a good effect in restoring image detail information.