Multi-Discriminator Image Restoration Algorithm Based on Hybrid Dilated Convolution Networks

With the continuous development of generative adversarial networks (GAN), many image restoration problems that are difficult to solve based on traditional methods have been given new research avenues. Nevertheless, there are still problems such as structural distortion and texture blurring of the complemented image in the face of irregular missing. In order to overcome these problems and retrieve the lost critical data of the image, a two-stage image restoration complementation network is proposed in this paper. While introducing hybrid dilation convolution, two attention mechanisms are added to the network and optimized using multiple loss functions. This not only results in better image quality metrics, but also clearer and more coherent image details. In this paper, we tested the network on CelebA-HQ, Places2 and The Paris datasets and compared it with several classical image restoration models, such as GLC, Gconv, Musical and RFR, and the results proved that the complementary images in this paper are improved compared to the others.

@article{Wu2024,
  title     = {Multi-Discriminator Image Restoration Algorithm Based on Hybrid Dilated Convolution Networks},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {4},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Chunming Wu and Fengshuo Qi},
  doi       = {10.14569/IJACSA.2024.0150434},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150434}
}