Single image dehazing network based on inception module

QianQian Huang; Qiong Cai; Yu Chen; JiaBao Huang

doi:10.1117/12.2628564

7 March 2022 Single image dehazing network based on inception module

QianQian Huang, Qiong Cai, Yu Chen, JiaBao Huang

Proceedings Volume 12167, Third International Conference on Electronics and Communication; Network and Computer Technology (ECNCT 2021); 121671X (2022) https://doi.org/10.1117/12.2628564
Event: 2021 Third International Conference on Electronics and Communication, Network and Computer Technology, 2021, Harbin, China

Abstract

Indoor and outdoor images captured by the camera are affected by atmospheric dust, haze, sand and other factors, resulting in graying of the images. Although existing image dehazing algorithms can achieve removal of haze, they suffer from incomplete dehazing and color distortion to some extent. To address the above problems, this paper proposes a singleimage dehazing network with multi-scale feature extraction. The network algorithm is based on recurrent generative adversarial network, and a feature extraction network model incorporating Inception module is proposed. The multiscale feature extraction network is incorporated into the generator of CycleGAN. And the residual network is combined to expand the perceptual field. Finally, the fused feature maps are reconstructed and restored to fog-free images by image restoration network. Compared with existing dehazing networks, the proposed network in this paper is more natural in the processing of image dehazing and has better results in image details as well as colors.

Citation Download Citation

QianQian Huang, Qiong Cai, Yu Chen, and JiaBao Huang "Single image dehazing network based on inception module", Proc. SPIE 12167, Third International Conference on Electronics and Communication; Network and Computer Technology (ECNCT 2021), 121671X (7 March 2022); https://doi.org/10.1117/12.2628564

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KEYWORDS

Feature extraction

Atmospheric modeling

Image restoration

Air contamination

Convolution

Image enhancement

Image processing

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