.png){kind=icon}
.png){kind=icon}
Return
Dual Multi-Scale Dehazing Network
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2023.3296592.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Single-image haze removal is a challenging ill-posed problem. Recently, methods based on training on synthetic data have achieved good dehazing results. However, we note that these methods can be further improved. A novel deep learning-based method is proposed to obtain a better-dehazed result for single-image dehazing in this paper. Specially, we propose a dual multi-scale network to learn the dehazing knowledge from synthetical data. The coarse multi-scale network is designed to capture a large variety of objects, and then fine multi-scale blocks are designed to capture a small variety of objects at each scale. To show the effectiveness of the proposed method, we perform experiments on a synthetic dataset and real hazy images. Extensive experimental results show that the proposed method outperforms the state-of-the-art methods.
Keywords:
Dual multi-scale
dehazing
synthetically data
deep learning
real haze image
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W