Single image super-resolution based on enhanced deep residual GAN

Zhiyong Chen; Jing Hu; Xuyang Zhang; Xiangjun Li

doi:10.1117/12.2541900

14 February 2020 Single image super-resolution based on enhanced deep residual GAN

Zhiyong Chen, Jing Hu, Xuyang Zhang, Xiangjun Li

Proceedings Volume 11430, MIPPR 2019: Pattern Recognition and Computer Vision; 114301W (2020) https://doi.org/10.1117/12.2541900
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China

Abstract

With the application of image more and more widely, People put forward higher requirements on the image quality of small objects and details in the image. In recent years, with the development of deep learning, it achieved good results in the research of image super-resolution. In this paper, we proposed EDSRGAN, a single image super-resolution(SISR) algorithm, based on enhanced residual network and the adversarial network. Compared with SRGAN, which is also based on the adversarial network, EDSRGAN can greatly reduce the high-frequency noise contained in the super-resolution(SR) image, and it also leads SRGAN in terms of peak signal to noise ratio and structural similarity evaluation indicators. Although EDSRGAN lagged behind EDSR in terms of peak signal to noise ratio and structural similarity, the SR images generated by EDSRGAN were sharper than EDSR in the object edges and targets details. EDSRGAN could achieve good results in image super-resolution on small targets.

Citation Download Citation

Zhiyong Chen, Jing Hu, Xuyang Zhang, and Xiangjun Li "Single image super-resolution based on enhanced deep residual GAN", Proc. SPIE 11430, MIPPR 2019: Pattern Recognition and Computer Vision, 114301W (14 February 2020); https://doi.org/10.1117/12.2541900

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KEYWORDS

Super resolution

Image enhancement

Gallium nitride

Image quality

Visualization

Convolution

Detection and tracking algorithms

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