Image Restoration of Landscape Design Based on DCGAN Optimization Algorithm

To enhance the quality and effectiveness of image restoration in landscape design, this study optimizes the existing methods for low efficiency and incomplete feature extraction in processing high-resolution and detail rich landscape design images. Firstly, based on the traditional generative adversarial network (GAN), a novel deep convolutional generative adversarial network (DCGAN) model is proposed. Subsequently, the model's ability to extract detailed features was enhanced by integrating dense connected networks (DenseNet) and compressed excitation networks (SENet) into the network architecture. An improved DCGAN is designed for the restoration of landscape design images. According to the results, the optimized model had a restoration precision and repair recall rate of 0.97 in benchmark performance testing, which was significantly better than traditional deep convolutional generative adversarial network models. In practical applications, the model had an average accuracy of over 97% in repairing four different styles of landscape images, with an average repair time as low as 0.06s. From this, it can be seen that the designed model can provide a more efficient technical means for the restoration and digital preservation of landscape design images.

@article{Zhang2024,
  title     = {Image Restoration of Landscape Design Based on DCGAN Optimization Algorithm},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {11},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Wenjun Zhang},
  doi       = {10.14569/IJACSA.2024.0151118},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151118}
}