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Image super-resolution reconstruction based on residual compensation combined attention network

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DOI: 10.23977/jeis.2023.080107 | Downloads: 35 | Views: 647

Author(s)

Xiyao Li 1

Affiliation(s)

1 College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, 730050, China

Corresponding Author

Xiyao Li

ABSTRACT

For image reconstruction, the residual network ignores part of the residual information when extracting features. We propose an image super-resolution reconstruction based on residual compensation joint attention network (RCCN). Firstly, we construct a three-way residual network for compensating the feature information of the standard residual network; secondly, we design a joint attention module to complement the pixel-level image attention information by 3D attention while the channel attention learns the channel weight information; finally, our method has clearer results compared with other advanced methods, and the objective evaluation indexes are all greatly improved.

KEYWORDS

Super-resolution reconstruction; Convolutional neural networks; Deep separable convolution; Residual networks; Attention mechanisms

CITE THIS PAPER

Xiyao Li, Image super-resolution reconstruction based on residual compensation combined attention network. Journal of Electronics and Information Science (2023) Vol. 8: 48-55. DOI: http://dx.doi.org/10.23977/10.23977/jeis.2023.080107.

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