Journal of Jilin University(Engineering and Technology Edition) ›› 2020, Vol. 50 ›› Issue (5): 1771-1777.doi: 10.13229/j.cnki.jdxbgxb20190443

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Image dehazing based on joint estimation via convolutional neural network

Ke-yan WANG(),Di WANG,Xi ZHAO,Jing-yi CHEN,Yun-song LI   

  1. State Key Laboratory of Integrated Service Networks, Xidian University, Xi'an 710071, China
  • Received:2019-05-09 Online:2020-09-01 Published:2020-09-16

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Abstract:

Images captured in outdoor scenes typically have lower contrast and visibility due to atmospheric particles suspended in the air, directly affecting the effectiveness of subjective vision and intelligent image processing systems. To solve this problem, we propose an end-to-end network, which jointly estimates transmission map and atmospheric light effectively. The global characteristics of transmittance and atmospheric light are acquired simultaneously by shared feature module, and the multi-scale convolution of the pyramid pooling module is utilized to extract the combination feature. Then, the transmittance and atmospheric light are separately estimated by two parallel branch networks. Finally, the fog-free image is obtained by inversion of the atmospheric scattering model. The experiment results show that proposed method removes haze more effectively compared to these state-of-the-art methods, and the restored image has higher contrast and more natural color. Moreover, the proposed network has less parameters.

Key words: information processing technology, image dehazing, convolutional neural network, joint estimation, atmospheric scattering model

CLC Number: 

  • TP751.1

Fig.1

Schematic diagram of atmospheric scattering model"

Fig.2

Transmission map and atmospheric light joint estimation algorithm chart"

Fig.3

Network structure diagram"

Fig.4

Pyramid pooling module structure diagram"

Fig.5

Dehazing comparison of algorithm based on deep learning on synthetic map"

Table 1

Synthetic haze map dehazing results comparison"

DatasetMetricsDehaze NetMSCNNBilinear -NetAOD-NetOurs
Make3DPSNR20.0122.6416.2119.3723.15
SSIM0.870.940.590.870.94
NYUPSNR19.0319.0514.1516.3419.05
SSIM0.860.660.860.800.87

Fig.6

Dehazing comparison of algorithm based on deep learning on real map"

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