• Corpus ID: 225103120

Learning Deep Interleaved Networks with Asymmetric Co-Attention for Image Restoration

@article{Li2020LearningDI,
  title={Learning Deep Interleaved Networks with Asymmetric Co-Attention for Image Restoration},
  author={Feng Li and Runmin Cong and Huihui Bai and Yifan He and Yao Zhao and Ce Zhu},
  journal={ArXiv},
  year={2020},
  volume={abs/2010.15689}
}
Recently, convolutional neural network (CNN) has demonstrated significant success for image restoration (IR) tasks (e.g., image super-resolution, image deblurring, rain streak removal, and dehazing). However, existing CNN based models are commonly implemented as a single-path stream to enrich feature representations from low-quality (LQ) input space for final predictions, which fail to fully incorporate preceding low-level contexts into later high-level features within networks, thereby… 
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