@article{3160,
  keywords = {Image Dehazing, Encoder and Decoder Network, Generative Adversarial Network, Multi-Scale Convolution Block, Loss Function},
  author = {Hongqi Zhang and Yixiong Wei and Hongqiao Zhou and Qianhao Wu},
  title = {ED-Dehaze Net: Encoder and Decoder Dehaze Network},
  abstract = {The presence of haze will significantly reduce the quality of images, such as resulting in lower contrast and blurry details. This paper proposes a novel end-to-end dehazing method, called Encoder and Decoder Dehaze Network (ED-Dehaze Net), which contains a Generator and a Discriminator. In particular, the Generator uses an Encoder-Decoder structure to effectively extract the texture and semantic features of hazy images. Between the Encoder and Decoder we use Multi-Scale Convolution Block (MSCB) to enhance the process of feature extraction. The proposed ED-Dehaze Net is trained by combining Adversarial Loss, Perceptual Loss and Smooth L1 Loss. Quantitative and qualitative experimental results showed that our method can obtain the state-of-the-art dehazing performance.},
  year = {2022},
  journal = {International Journal of Interactive Multimedia and Artificial Intelligence},
  volume = {7},
  number = {5},
  pages = {93-99},
  month = {09/2022},
  issn = {1989-1660},
  url = {https://www.ijimai.org/journal/sites/default/files/2022-08/ijimai_7_5_11.pdf},
  doi = {10.9781/ijimai.2022.08.008},
}