基于生成对抗网络的多特征融合去雾技术

doi:10.11959/j.issn.2096-0271.2024047

Abstract

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.

Key words: image processing, image dehazing, deep learning, generative adversarial, multiple feature fusion

CLC Number:

TP317.4

Yazhong SI, Xulong ZHANG, Fan YANG, Jianzong WANG, Ning CHENG, Jing XIAO. Multiple-feature fusion based generative adversarial network for image dehazing[J]. Big Data Research, 2024, 10(4): 77-88.

Figures/Tables 10

场景类别	DCP	MSBDN	DehazeFormer	T-Net	本文方法
场景类别	PSNR(dB)/SSIM	PSNR(dB)/SSIM	PSNR(dB)/SSIM	PSNR(dB)/SSIM	PSNR(dB)/SSIM
bright	12.8324/71.91%	23.6712/93.91%	20.3518/90.82%	$\underline{27.9463}$ / $\underline{95.68 %}$	$29 . 8192 / 96 . 50 %$
dark	15.9842/75.13%	24.1169/77.46%	20.9747/90.53%	$\underline{24.593}$ / $\underline{90.79 %}$	$31 . 2793 / 94 . 60 %$
mist	12.6756/72.11%	$\underline{26.782}$ /94.23%	20.4727/89.79%	26.1434/ $\underline{95.98 %}$	$32 . 7965 / 97 . 10 %$
thick	12.9062/81.32%	$\underline{23.2929}$ / $\underline{94.19 %}$	21.2429/92.50%	19.6503/86.82%	$27 . 821 / 95 . 65 %$
注：加粗数值为每行最优值，下划线标注数值为次优值。

Dehaze method	PSNR(dB)	SSIM
DCP	14.2992	63.57%
MSBDN	16.0611	68.38%
DehazeFormer	14.9886	62.08%
T-Net	16.1393	67.07%
本文方法	$16 . 5383$	$71 . 90 %$
注：加粗数值为每行最优值，下划线标注数值为次优值。

References 20

[1]	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.
[2]	STARK J A . Adaptive image contrast enhancement using generalizations of histogram equalization[J]. IEEE Transactions on Image Processing, 2000,9(5): 889-896.
[3]	JOBSON D J . Retinex processing for automatic image enhancement[J]. Journal of Electronic Imaging, 2004,13(1): 100.
[4]	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.
[5]	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.
[6]	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.
[7]	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.
[8]	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.
[9]	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.
[10]	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.
[11]	ULYANOV D , VEDALDI A , LEMPITSKY V ,et al. Instance normalization:the missing ingredient for fast stylization[EB]. arXiv preprint,2016,arXiv:1607.08022.
[12]	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.
[13]	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.
[14]	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.
[15]	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.
[16]	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.
[17]	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.
[18]	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.
[19]	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.
[20]	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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