Multiple-feature fusion based generative adversarial network for image dehazing

Yazhong SI; Xulong ZHANG; Fan YANG; Jianzong WANG; Ning CHENG; Jing XIAO

doi:10.11959/j.issn.2096-0271.2024047

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Multiple-feature fusion based generative adversarial network for image dehazing

STUDY | 更新时间：2024-08-07

- Multiple-feature fusion based generative adversarial network for image dehazing
- Big data research Vol. 10, Issue 4, Pages: 77-88(2024)
- 作者机构：
  
  1. 平安科技（深圳）有限公司，广东深圳 518063
  2. 河北工业大学，天津 300401
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-0271.2024047
  CLC： TP317.4
- Online First：2024-07，
  
  Published：15 July 2024
- 稿件说明：
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Yazhong SI, Xulong ZHANG, Fan YANG, et al. Multiple-feature fusion based generative adversarial network for image dehazing[J]. Big data research, 2024, 10(4): 77-88.
DOI：

Yazhong SI, Xulong ZHANG, Fan YANG, et al. Multiple-feature fusion based generative adversarial network for image dehazing[J]. Big data research, 2024, 10(4): 77-88. DOI： 10.11959/j.issn.2096-0271.2024047.

摘要

为提高图像清晰度，解决传统图像在去雾过程中存在的特征提取困难、去雾不彻底等问题，提出一种基于生成对抗网络的多特征融合端到端去雾网络。该网络由生成器和判别器组成，生成器采用编解码结构，通过多特征提取融合（MFEF）块提取多种感受野下的高维表征信息。判别器使用一系列卷积计算对生成图像和清晰图像进行特征差异分析，引导生成器输出更加真实的去雾图像。实验结果表明，该算法在有效消除雾霾干扰的同时，能够最大限度地保留图像的原始色调。与现有方法相比，该算法在峰值信噪比、结构相似性客观评价指标上分别提升了2.588 dB、2.66%。

Abstract

To enhance image clarity and address the difficulties in feature extraction and incomplete haze removal in traditional image dehazing processes

a multi-feature fusion based generative adversarial dehazing network is proposed.The network adopts a generative adversarial approach and consists of a generator and a discriminator.The generator utilizes an encoderdecoder structure

and extracts haze-related feature maps from multiple receptive fields by a multi-feature extraction fusion (MFEF) block.The discriminator uses a series of convolutional calculations to analyze the feature differences between the generated images and the clear images

guiding the generator to output move realistic dehazing images.The experimental images show that the proposed method can effectively eliminate haze interference while preserving the original color tone of the image to the greatest extent possible.The experimental results demonstrate that the dehazed images produced by our algorithm have improved peak signal-to-noise ratio and structural similarity with an average of 2.588 dB and 2.66% respectively

compared with existing methods.

关键词

Keywords

references

禹晶 , 徐东彬 , 廖庆敏 . 图像去雾技术研究进展 [J ] . 中国图象图形学报 , 2011 , 16 ( 9 ): 1561 - 1576 .

YU J , XU D B , LIAO Q M . Image defogging:a survey [J ] . Journal of Image and Graphics , 2011 , 16 ( 9 ): 1561 - 1576 .

STARK J A . Adaptive image contrast enhancement using generalizations of histogram equalization [J ] . IEEE Transactions on Image Processing , 2000 , 9 ( 5 ): 889 - 896 .

JOBSON D J . Retinex processing for automatic image enhancement [J ] . Journal of Electronic Imaging , 2004 , 13 ( 1 ): 100 .

NARASIMHAN S G , NAYAR S K . Contrast restoration of weather degraded images [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2003 , 25 ( 6 ): 713 - 724 .

HE K M , SUN J , TANG X O . Single image haze removal using dark channel prior [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2011 , 33 ( 12 ): 2341 - 2353 .

刘万军 , 白宛司 , 曲海成 , 等 . 融合GFMSRCR和暗通道先验的图像去雾 [J ] . 中国图象图形学报 , 2019 , 24 ( 11 ): 1893 - 1905 .

LIU W J , BAI W S , QU H C , et al . Image dehazing based on GF-MSRCR and dark channel prior [J ] . Journal of Image and Graphics , 2019 , 24 ( 11 ): 1893 - 1905 .

CAI B L , XU X M , JIA K , et al . DehazeNet:an end-to-end system for single image haze removal [J ] . IEEE Transactions on Image Processing , 2016 , 25 ( 11 ): 5187 - 5198 .

LI B Y , PENG X L , WANG Z Y , et al . AOD-net:all-in-one dehazing network [C ] // Proceedings of the 2017 IEEE International Conference on Computer Vision . Piscataway:IEEE Press , 2017 : 4780 - 4788 .

CHEN D D , HE M M , FAN Q N , et al . Gated context aggregation network for image dehazing and deraining [C ] // Proceedings of the 2019 IEEE Winter Conference on Applications of Computer Vision . Piscataway:IEEE Press , 2019 : 1375 - 1383 .

DONG Y , LIU Y H , ZHANG H , et al . FDGAN:generative adversarial networks with fusion-discriminator for single image dehazing [J ] . Proceedings of the AAAI Conference on Artificial Intelligence.California:AAAI , 2020 , 34 ( 7 ): 10729 - 10736 .

ULYANOV D , VEDALDI A , LEMPITSKY V , et al . Instance normalization:the missing ingredient for fast stylization [EB ] . arXiv preprint,2016,arXiv:1607.08022 .

GOODFELLOW I J , POUGET-ABADIE J , MIRZA M , et al . Generative adversarial nets [C ] // Proceedings of the Proceedings of the 27th International Conference on Neural Information Processing Systems Volume 2 . Cambridge:MIT Press , 2014 : 2672 - 2680 .

HE K M , ZHANG X Y , REN S Q , et al . Deep residual learning for image recognition [C ] // Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2016 : 770 - 778 .

ISOLA P , ZHU J Y , ZHOU T H , et al . Image-to-image translation with conditional adversarial networks [C ] // Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2017 : 5967 - 5976 .

SILBERMAN N , HOIEM D , KOHLI P , et al . Indoor segmentation and support inference from RGBD images [C ] // Proceedings of the 12th European Conference on Computer Vision . Heidelberg:Springer , 2012 : 746 - 760 .

LI B Y , REN W Q , FU D P , et al . Benchmarking single image dehazing and beyond [J ] . IEEE Transactions on Image Processing , 2018 , 28 ( 1 ): 492 - 505 .

ANCUTI C , ANCUTI C O , TIMOFTE R , et al . NTIRE 2018 challenge on image dehazing:methods and results [C ] // Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops . Piscataway:IEEE Press , 2018 : 1004 - 100410 .

DONG H , PAN J S , XIANG L , et al . Multi-scale boosted dehazing network with dense feature fusion [C ] // Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2020 : 2154 - 2164 .

SONG Y D , HE Z Q , QIAN H , et al . Vision transformers for single image dehazing [J ] . IEEE Transactions on Image Processing , 1927 , 32 : 1927 - 1941 .

ZHENG L R , LI Y S , ZHANG K H , et al . T-net:deep stacked scale-iteration network for image dehazing [J ] . IEEE Transactions on Multimedia , 2022 , 25 : 6794 - 6807 .

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