Combination of Dark-channel Prior with Sparse Representation for Underwater Image Restoration

WANG Xin; ZHU Hangcheng; NING Chen; Lü Guofang

doi:10.11999/JEIT170381

Volume 40 Issue 2

Feb. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(2): 264-271

Qian Zhi-Hong, Guo Yu-Qi, Hou Jin-Feng, Wang Yi-Jun. Co-channel Interference Suppression Method Based on Channel Switching for Bluetooth Piconets[J]. Journal of Electronics & Information Technology, 2011, 33(12): 2995-3001. doi: 10.3724/SP.J.1146.2011.00634

Citation:

WANG Xin, ZHU Hangcheng, NING Chen, Lü Guofang. Combination of Dark-channel Prior with Sparse Representation for Underwater Image Restoration[J]. Journal of Electronics & Information Technology, 2018, 40(2): 264-271. doi: 10.11999/JEIT170381

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Combination of Dark-channel Prior with Sparse Representation for Underwater Image Restoration

doi: 10.11999/JEIT170381

1.
(College of Computer and Information, Hohai University, Nanjing 211100, China)
2.
(School of Physics and Technology, Nanjing Normal University, Nanjing 210000, China)

Funds:

The National Natural Science Foundation of China (61374019, 61603124), The Fundamental Research Funds for the Central Universities (2015B19014), 333 High-Level Talent Training Program of Jiangsu Province, Six Talents Peak Project of Jiangsu Province (XYDXX-007)

Received Date: 2017-04-25
Rev Recd Date: 2017-09-12
Publish Date: 2018-02-19

Abstract

Abstract

Due to the influences of scattering of the light and interference of the noise, underwater image quality is always degraded severely. In order to remove the blur and suppress the noise, and improve the quality of underwater image, a novel underwater image restoration method based on the combination of dark-channel prior with sparse representation is proposed. This method adopts the dark-channel prior theory to calculate the dark-channel image at first, and then uses sparse representation to denoise and optimize the dark-channel image. Based on the improved dark-channel image, the more precise water transmissivity and light intensity can be achieved to compute the final restoration result, effectively eliminating the image blur as well as noise. The experimental results show that the proposed method can effectively improve the image factors, such as average gradient and entropy, so as to compensate the degraded image.
- Underwater image restoration,
- Dark-channel prior,
- Sparse representation

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References(22)

References

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