High-Resolution Representations Network for Single Image Dehazing

Deep learning-based image dehazing methods have made great progress, but there are still many problems such as inaccurate model parameter estimation and preserving spatial information in the U-Net-based architecture. To address these problems, we propose an image dehazing network based on the high-r...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2022-03, Vol.22 (6), p.2257
Main Authors: Han, Wensheng, Zhu, Hong, Qi, Chenghui, Li, Jingsi, Zhang, Dengyin
Format: Article
Language:English
Subjects:
Algorithms
Deep learning
Feature maps
High resolution
high-resolution representations
image dehazing
image restoration
Light
Methods
Neural networks
Parameter estimation
Pose estimation
Representations
Spatial data
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Summary:Deep learning-based image dehazing methods have made great progress, but there are still many problems such as inaccurate model parameter estimation and preserving spatial information in the U-Net-based architecture. To address these problems, we propose an image dehazing network based on the high-resolution network, called DeHRNet. The high-resolution network originally used for human pose estimation. In this paper, we make a simple yet effective modification to the network and apply it to image dehazing. We add a new stage to the original network to make it better for image dehazing. The newly added stage collects the feature map representations of all branches of the network by up-sampling to enhance the high-resolution representations instead of only taking the feature maps of the high-resolution branches, which makes the restored clean images more natural. The final experimental results show that DeHRNet achieves superior performance over existing dehazing methods in synthesized and natural hazy images.
ISSN:1424-8220
1424-8220
DOI:10.3390/s22062257