Deep Multi-Model Fusion for Single-Image Dehazing

Zijun Deng, Lei Zhu, Xiaowei Hu, Chi-Wing Fu, Xuemiao Xu, Qing Zhang, Jing Qin, Pheng-Ann Heng; The IEEE International Conference on Computer Vision (ICCV), 2019, pp. 2453-2462

Abstract


This paper presents a deep multi-model fusion network to attentively integrate multiple models to separate layers and boost the performance in single-image dehazing. To do so, we first formulate the attentional feature integration module to maximize the integration of the convolutional neural network (CNN) features at different CNN layers and generate the attentional multi-level integrated features (AMLIF). Then, from the AMLIF, we further predict a haze-free result for an atmospheric scattering model, as well as for four haze-layer separation models, and then fuse the results together to produce the final haze-free image. To evaluate the effectiveness of our method, we compare our network with several state-of-the-art methods on two widely-used dehazing benchmark datasets, as well as on two sets of real-world hazy images. Experimental results demonstrate clear quantitative and qualitative improvements of our method over the state-of-the-arts.

Related Material


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[bibtex]
@InProceedings{Deng_2019_ICCV,
author = {Deng, Zijun and Zhu, Lei and Hu, Xiaowei and Fu, Chi-Wing and Xu, Xuemiao and Zhang, Qing and Qin, Jing and Heng, Pheng-Ann},
title = {Deep Multi-Model Fusion for Single-Image Dehazing},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}