Multi-Level Fusion and Attention-Guided CNN for Image Dehazing

• Published in: IEEE transactions on circuits and systems for video technology Vol. 31; no. 11; pp. 4162 - 4173
• Main Authors: Zhang, Xiaoqin, Wang, Tao, Luo, Wenhan, Huang, Pengcheng
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Atmospheric modeling; attention module; CNN; Convolution; Datasets; Feature extraction; Image dehazing; Image restoration; Luminous intensity; Modules; multi-level fusion; Scattering; Semantics; Task analysis
• ISSN: 1051-8215; 1558-2205
• DOI: 10.1109/TCSVT.2020.3046625

In this paper, we tackle the problem of single image dehazing with a convolutional neural network. Within this network, we develop a multi-level fusion module to utilize both low-level and high-level features. The low-level features help to recover finer details, and the high-level features discover abstract semantics. They are complementary in the restoring of clear images. Moreover, a Residual Mixed-convolution Attention Module (RMAM) with an attention block is proposed to guide the network to focus on important features in the learning process. In this RMAM, group convolution, depth-wise convolution, and point-wise convolution are mixed, and thus it is much faster than its counterparts. With these two modules, we thus have an end-to-end network without explicitly estimating the atmospheric light intensity and the transmission map in the classical atmosphere scattering model. Both qualitative and quantitative experimental studies are carried out on public datasets including RESIDE, DCPDN-TestA, and the real-world dataset. The extensive results demonstrate both the effectiveness and efficiency of the proposed solution to single image dehazing.