Multi-scale Generative Adversarial Deblurring Network with Gradient Guidance

Jinxiu Zhu,
Xue Xu,
Chang Choi,
Xin Su,

Abstract


With regards to the lack of crisp edges and a poor recovery of high frequency information such as details in deblurred motion pictures, this research proposes a multi-scale adversarial deblurring network with gradient guidance (MADN). The algorithm uses the classical generative adversarial network (GAN) framework, consisting of a generator and a discriminator. The generator includes a multi-scale convolutional network and a gradient feature extraction network. The multi-scale convolutional network extracts image features at different scales with a nested connection residual codec structure to improve the image edge structure recovery and to increase the perceptual field. This gradient network incorporates with intermediate scale features to extract the gradient features of blurred images to obtain their high frequency information. The generator combines the gradient and multiscale features to recover the remaining high-frequency information in a deblurred image. The loss function of MADN is formed in this research combining adversarial loss, pixel L2-norm loss and mean absolute error. Compared to those experimental results obtained from current deblurring algorithms, our experimental results indicate visually clearer images retaining more information such as edges and details. This MADN algorithm enhances the peak signal-to-noise ratio by an average of 3.32dB and the structural similarity by an average of 0.053.

Keywords


Motion deblurring, Multi-scale network, Nested residual connection, GAN, Gradient feature extraction

Citation Format:
Jinxiu Zhu, Xue Xu, Chang Choi, Xin Su, "Multi-scale Generative Adversarial Deblurring Network with Gradient Guidance," Journal of Internet Technology, vol. 24, no. 2 , pp. 243-255, Mar. 2023.

Full Text:

PDF

Refbacks

  • There are currently no refbacks.





Published by Executive Committee, Taiwan Academic Network, Ministry of Education, Taipei, Taiwan, R.O.C
JIT Editorial Office, Office of Library and Information Services, National Dong Hwa University
No. 1, Sec. 2, Da Hsueh Rd., Shoufeng, Hualien 974301, Taiwan, R.O.C.
Tel: +886-3-931-7314  E-mail: jit.editorial@gmail.com