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

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(10): 2509-2514

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

Citation:

PDF( 41425 KB)

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

doi: 10.11999/JEIT160208 cstr: 32379.14.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

FullText(HTML)

References(18)

References

HE Kaiming, SUN Jian, and TANG Xiaoou. Single image haze removal using dark channel prior[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(12): 2341-2353. doi: 10.1109/TPAMI.2010.168.

毕笃彦, 葛渊, 李权合, 等. 单幅图像去雾方法研究[J]. 空军工程大学学报: 自然科学版, 2013, 14(6): 46-53. doi: 10.3969/j.issn.1009-3516.2013.06.012.

BI Duyan, GE Yuan, LI Quanheng, et al. A research on defogging methods with single image[J]. Journal of Air Force Engineering University: Natural Science Edition, 2013, 14(6): 46-53. doi: 10.3969/j.issn.1009-3516.2013.06.012.

MI Z, ZHOU H, ZHENG Y, et al. Single image dehazing via multi-scale gradient domain contrast enhancement[J]. IET Image Processing, 2016, 3(10): 206-214. doi: 10.1049/iet-ipr. 2015.0112.

HE R, WANG Z, FAN Y, et al. Combined constraint for single image dehazing[J]. IET Electronics Letters, 2015, 51(22): 1776-1778. doi: 10.1049/el.2015.0707.

WANG Dan and ZHU Jubo. Fast smoothing technique with edge preservation for single image dehazing[J]. IET Computer Vision, 2015, 9(6): 950-959. doi: 10.1049/iet-cvi.2015.0063.

WANG Jinbao, HE Ning, ZHANG Lulu, et al. Single image dehazing with a physical model and dark channel prior[J]. Elsevier Neurocomputing, 2015, 149(PB): 718-728. doi: 10.1016/j.neucom.2014.08.005.

WANG Zhongliang and FENG Yan. Fast single haze image enhancement[J]. Elsevier Computers Electrical Engineering, 2014, 40(3): 785-795. doi: 10.1016/j.compeleceng.2013.06. 009.

NARASIMHAN S and NAYAR S. Vision and the atmosphere[J]. International Journal of Computer Vision, 2002, 48(3): 233-254. doi: 10.1023/A:1016328200723.

TAN R. Visibility in bad weather from a single image[C]. IEEE Conference on Computer Vision and Pattern Recognition, Alaska, USA, 2008: 1-8.

FATTAL R. Single image dehazing[J]. ACM Transactions on Graphics, 2008, 27(3): 1-9. doi: 10.1145/1399504.1360671.

TAREL J and HAUTIERE N. Fast visibility restoration from a single color or gray level image[C]. IEEE International Conference on Computer Vision, Kyoto, Japan, 2009: 2201-2208.

RUDIN L, OSHER S, and FATEMI E. Nonlinear total variation based noise removal algorithms[J]. Physica D: Nonlinear Phenomena, 1992, 60(1): 259-268. doi: 10.1016/ 0167-2789(92)90242-F.

吴迪, 朱青松. 图像去雾的最新研究进展[J]. 自动化学报, 2015, 41(2): 221-239. doi: 10.16383/j.aas.2015.c131137.

WU Di and ZHU Qingsong. The latest research progress of image dehazing[J]. Acta Automatica Sinica, 2015, 41(2): 221-239. doi: 10.16383/j.aas.2015.c131137.

SULAMI M, GELTZER I, FATTAL R, et al. Automatic recovery of the atmospheric light in hazy images[C]. IEEE International Conference on Computational Photography, Santa Clara, USA, 2014: 1-11.

MITTAL A, MOORTHY A, and BOVIK A. No-reference image quality assessment in the spatial domain[J]. IEEE Transactions on Image Processing, 2012, 21(12): 4695-4708. doi: 10.1109/TIP.2012.2214050.

HAUTIERE N, TAREL J, and AUBERT D. Blind contrast enhancement assessment by gradient ratioing at visible edges[J]. Image Analysis Stereology Journal, 2008, 27(2): 87-95. doi: 10.5566/ias.v27.p87-95.

Relative Articles

Supplements(0)

Cited By

Proportional views