Novel Single Hazy Image Restoration Method Based on Nonlocal Total Variation Regularization Optimization

HE Renjie; FAN Yangyu; WANG Zhiyong; FENG David

doi:10.11999/JEIT160208

Volume 38 Issue 10

Oct. 2016

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HE Renjie, FAN Yangyu, WANG Zhiyong, FENG David. Novel Single Hazy Image Restoration Method Based on Nonlocal Total Variation Regularization Optimization[J]. Journal of Electronics & Information Technology, 2016, 38(10): 2509-2514. doi: 10.11999/JEIT160208

Citation:

HE Renjie, FAN Yangyu, WANG Zhiyong, FENG David. Novel Single Hazy Image Restoration Method Based on Nonlocal Total Variation Regularization Optimization[J]. Journal of Electronics & Information Technology, 2016, 38(10): 2509-2514. doi: 10.11999/JEIT160208

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Novel Single Hazy Image Restoration Method Based on Nonlocal Total Variation Regularization Optimization

doi: 10.11999/JEIT160208

1.
2.
(Shaanxi Key Laboratory of Information Acquisition and Processing, The School of Electronics and Information, Northwestern Polytechnical University, Xi&rsquo

Funds:

The National Natural Science Foundation of China (61420106007), Shaanxi Science Technology Co-ordination Innovation Project

Received Date: 2016-03-07
Rev Recd Date: 2016-07-14
Publish Date: 2016-10-19

Abstract

Abstract

Based on the property that the scene radiance is of high contrast and the atmospheric veil is locally smooth, a novel single hazy image restoration method based on nonlocal total variation regularization optimization is proposed in this paper. In order to obtain the atmospheric veil of a hazy image, a constrained nonlocal total variation regularization is firstly applied. Then, the accurate atmospheric veil is estimated using a nonlocal Rudin- Osher-Fatemi model, which is solved by a modified split Bregman method. Experimental results demonstrate that the proposed approach is capable of recovering the scene radiance from a single hazy image effectively, especially for the regions with multi-texture.
- Image processing,
- Image dehazing,
- Contrast enhancement,
- Regularization optimization

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References(18)

References

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