Dual-domain Feature Learning and Cross Dimension Interaction Attention for Nighttime Image Dehazing
Article No.: 68, Pages 1 - 7
Abstract
Nighttime image dehazing is critical for many computer applications. Directly transferring daytime dehazing models to nighttime scenes often introduces haze residual, detail loss and color distortion for the uneven distribution by artificial lights. Therefore, we propose a nighttime dehazing method by defining the Dual-domain Feature Learning Module (DFLM) and the Feature Optimization Module (FOM). Firstly, we construct the DFLM in both frequency and spatial domains to accurately predict the image degradation caused by haze and remove most haze in nighttime hazy images. Secondly, to address the challenges of uneven illumination distribution and color interference of light sources in nighttime, we construct the FOM based on the proposed Cross Dimension Interaction Attention (CDIA), which captures the feature dependencies by crossing different dimensions including the channel-channel, height-channel and width-channel. By precisely representing illumination and color features, the FOM alleviates color distortion in nighttime dehazing. Extensive experiments on several synthetic and real-world datasets demonstrate that our method outperforms most state-of-the-art methods. Code will be available.
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Index Terms
- Dual-domain Feature Learning and Cross Dimension Interaction Attention for Nighttime Image Dehazing
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Published In
December 2023
745 pages
ISBN:9798400702051
DOI:10.1145/3595916
Copyright © 2023 ACM.
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Published: 01 January 2024
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- Meizhou Tobacco Technology Project of Guangdong Province
- the key R&D project of Guangzhou
- the Science and Technology Planning Project of Guangdong Province
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MMAsia '23
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