DEA-Net: Single Image Dehazing Based on Detail-Enhanced Convolution and Content-Guided Attention
Zhejiang University · Nanjing University of Aeronautics and Astronautics
Abstract
Single image dehazing is a challenging ill-posed problem which estimates latent haze-free images from observed hazy images. Some existing deep learning based methods are devoted to improving the model performance via increasing the depth or width of convolution. The learning ability of Convolutional Neural Network (CNN) structure is still under-explored. In this paper, a Detail-Enhanced Attention Block (DEAB) consisting of Detail-Enhanced Convolution (DEConv) and Content-Guided Attention (CGA) is proposed to boost the feature learning for improving the dehazing performance. Specifically, the DEConv contains difference convolutions which can integrate prior information to complement the vanilla one and enhance…
Citation impact
- FWCI
- 158.41
- Percentile
- 100%
- References
- 47
Authors
3Topics & keywords
- Computer science
- Convolution (computer science)
- Artificial intelligence
- Block (permutation group theory)
- Feature (linguistics)
- Convolutional neural network
- Pattern recognition (psychology)
- Fuse (electrical)