research-article
Authors: Chenyi Wang, Xiaotao Shao, Yan Shen
CSAIDE '24: Proceedings of the 2024 3rd International Conference on Cyber Security, Artificial Intelligence and Digital Economy
Pages 178 - 182
Published: 24 July 2024 Publication History
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Abstract
Existing image dehazing methods have low quality in dealing with non-uniform haze in real scenes, especially in heavy haze situations. To address this problem, we propose a Multi-Level Feature Extraction Improvement Network (MLFEIN), which effectively removes non-uniform haze in images by extracting and fusing features at different stages and levels. Specifically, we design an Efficient Feature Fusion (EFF) which has an encoder-decoder structure with fusion mechanism, and a Channel and Spatial Information Enhancement (CSIE) module that can efficiently extract features. The EFF module can preserve the image structure information and eliminate artifacts caused by non-uniform haze by combining high-level and low-level features. The CSIE module can further eliminate non-uniform haze by using two separate attention blocks to generate information maps, which utilize spatial and channel information respectively. Moreover, we add residual structures before the attention mechanism to prevent network degradation and enhance global feature extraction. Extensive experiments show that our method achieves better results than the state-of-the-art methods.
References
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Digital Library
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Index Terms
Non-hom*ogeneous Haze Removal Based on Attentional Feature Enhancement in Encoder-decoder Networks
Theory of computation
Models of computation
Streaming models
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Abstract
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Graphical abstract
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Highlights
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CSAIDE '24: Proceedings of the 2024 3rd International Conference on Cyber Security, Artificial Intelligence and Digital Economy
March 2024
676 pages
ISBN:9798400718212
DOI:10.1145/3672919
Copyright © 2024 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [emailprotected].
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 24 July 2024
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CSAIDE 2024
March 1 - 3, 2024
Nanjing, China
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