Image Super-Resolution Via Gradient Guidance and Gnenrative Adversarial Network

Authors

  • Dongmei Ma
  • Yu Li

DOI:

https://doi.org/10.54097/jceim.v10i1.5074

Keywords:

Image super-resolution, Structural distortion, WGAN-GP, Gradient guidance, Adversarial training, Multi-scale residual block

Abstract

In order to solve the problem that the existing image super-resolution reconstruction model generates poor visual quality and prone to structural distortion, a deep gradient guidance based on generative adversarial network is proposed. The generator introduces the gradient branch to transfer the features of the gradient image and fuses the gradient information with the image branch to prevent the image edge distorted. Referring to MSRB, ResNext and Inception, an improved multi-scale residual block is proposed and applied to the basic module of the image branch and gradient branch, which makes model easier to obtain multi-scale information. The discriminator uses WGAN-GP to improve the stability of network training. Compared with the perceptual-driven algorithms such as SRGAN, ESRGAN and NatSR, the proposed algorithm can effectively prevent structural distortion of the image and improve the quality of the generated image. The computational complexity of proposed model is 23.7GFLOPs, which is lower than ESRGAN and SPSR about 1/4 and 1/10.

References

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Published

07-02-2023

Issue

Section

Articles

How to Cite

Ma, D., & Li, Y. (2023). Image Super-Resolution Via Gradient Guidance and Gnenrative Adversarial Network. Journal of Computing and Electronic Information Management, 10(1), 12-16. https://doi.org/10.54097/jceim.v10i1.5074

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