Image Dehazing With Contextualized Attentive U-NET


Lee, Yean Wei and Wong, Lai Kuan and See, John Su Yang (2020) Image Dehazing With Contextualized Attentive U-NET. In: International Conference on Image Processing, 25-28 Oct. 2020, Abu Dhabi, United Arab Emirates.

[img] Text
Restricted to Repository staff only

Download (251kB)


Haze, which occurs due to the accumulation of fine dust or smoke particles in the atmosphere, degrades outdoor imaging, resulting in reduced attractiveness of outdoor photography and the effectiveness of vision-based systems. In this paper, we present an end-to-end convolutional neural network for image dehazing. Our proposed U-Net based architecture employs Squeeze-and-Excitation (SE) blocks at the skip connections to enforce channel-wise attention and parallelized dilated convolution blocks at the bottleneck to capture both local and global context, resulting in a richer representation of the image features. Experimental results demonstrate the effectiveness of the proposed method in achieving state-of-the-art performance on the benchmark SOTS dataset

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Neural networks (Computer science), Image dehazing, deep neural network, CNN, U-Net, dilated convolution
Subjects: Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions: Faculty of Computing and Informatics (FCI)
Depositing User: Ms Rosnani Abd Wahab
Date Deposited: 12 Sep 2021 13:55
Last Modified: 12 Sep 2021 13:55


Downloads per month over past year

View ItemEdit (login required)