Image Dehazing Based on Generative Adversarial Network
HUANG Shuying1, WANG Bin2, LI Hongxia2, YANG Yong3, HU Wei3
1. School of Computer Science and Technology, Tiangong University, Tianjin 300387 2. School of Software and Internet of Things Engineering, Jiangxi University of Finance and Economics, Nanchang 330013 3. School of Information Management, Jiangxi University of Finance and Economics, Nanchang 330032
Abstract Compared with the image dehazing methods based on image enhancement or physical model, the current image dehazing methods based on deep learning improve the computational efficiency to a certain extent. Nevertheless, the problems of incomplete dehazing and color distortion still exist in complex scenes. Aiming at the different perceptions of human eyes on global and local features, an algorithm of image dehazing based on generative adversarial networks is proposed. Firstly, a multi-scale generator network is designed. The full-size image and the segmented image block are taken as the input to extract the global contour information and local detail information of the image. Then, a feature fusion module is constructed to fuse the global and local information, and the authenticity of the generated dehazing image is judged by the discriminant network. To make the generated dehazing image closer to the corresponding real haze-free image, a multivariate joint loss function is designed by combining the dark channel prior loss, the adversarial loss, the structural similarity loss and the smooth L1 loss to train the network. Experimental results show that the proposed algorithm is superior to some state-of-the-art dehazing algorithms.
Fund:National Natural Science Foundation of China(No.61862030,62072218), Natural Science Foundation of Jiangxi Province(No.20192ACB20002,20192ACBL21008)
Corresponding Authors:
YANG Yong, Ph.D., professor. His research interests include image processing and deep learning.
About author:: HUANG Shuying, Ph.D., associate professor. Her research interests include image processing and machine learning. WANG Bin, master student. His research interests include deep learning, low-quality image enhancement and image dehazing. LI Hongxia, master student. Her research interests include image dehazing and low-light image enhancement. HU Wei, master student. His research interests include image dehazing and image enhancement.
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2021, Vol. 34 
