SRM-Net: An Effective End-to-end Neural Network for Single Image Dehazing
Authors: 
Yu Li, Wenbin Yu, Lingya Liu, Yiyin Wang, Lu DingAuthors Info & Claims
Pages 74 - 78
Abstract
Recently, the great development of deep learning has prompted many neural networks for single image dehazing to occur. How-ever, due to the ill-posed nature of haze, an excellent charac-teristics representation capacity is still challenging. In this paper, we propose a lightweight yet effective senet-residual (SE-Res) multiscale end-to-end neural network named SRM-Net. Inspired by the remarkable performance of residual networks, we intro-duce a SE-Res structure which is an improved residual framework with an embedded SE unit to obtain feature maps. These maps pass through a multiscale mapping layer which can aggregate characteristics in different receptive fields. Notably, the utilization of all point-wise convolutions in the SRM-Net leads to fewer parameters for training, and the reuse of feature maps makes it more lightweight. Through extensive numerical experiments on three datasets including real hazy images, synthetic indoor and outdoor hazy images, the proposed SRM-Net achieves superior performances on subjective visual results and objective evaluation metrics compared to the state-of-the-art methods.
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- SRM-Net: An Effective End-to-end Neural Network for Single Image Dehazing
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Published In
December 2019
270 pages
ISBN:9781450376822
DOI:10.1145/3376067
Copyright © 2019 ACM.
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In-Cooperation
- Shanghai Jiao Tong University: Shanghai Jiao Tong University
- Xidian University
- TU: Tianjin University
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 25 February 2020
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ICVIP 2019
ICVIP 2019: 2019 the 3rd International Conference on Video and Image Processing
December 20 - 23, 2019
Shanghai, China
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