融合暗原色先验和稀疏表示的水下图像复原

王鑫; 朱行成; 宁晨; 吕国芳

doi:10.11999/JEIT170381

融合暗原色先验和稀疏表示的水下图像复原

doi: 10.11999/JEIT170381

1.
(河海大学计算机与信息学院南京 211100)
2.
(南京师范大学物理科学与技术学院南京 210000)

基金项目:

国家自然科学基金面上项目(61374019)，国家自然科学基金青年基金(61603124)，教育部中央高校基本科研业务费专项资金(2015B19014)，江苏省333高层次人才培养工程，江苏省六大人才高峰高层次人才项目(XYDXX-007)

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出版历程
- 收稿日期: 2017-04-25
- 修回日期: 2017-09-12
- 刊出日期: 2018-02-19

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

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)

摘要

摘要: 由于水下图像成像过程中受光的散射、噪声干扰等因素影响，致使图像质量严重退化。为了去除模糊和抑制噪声，改善水下图像质量，该文提出一种融合暗原色先验和稀疏表示的水下图像复原新方法。该方法首先利用暗原色先验理论计算水下图像的暗原色，然后基于稀疏表示理论对暗原色进行去噪和优化，基于改进后的暗原色计算水体透射率和光照强度以计算最终复原结果，可以同时达到去模糊和去噪的良好效果。实验结果表明，提出的方法有效提高了图像的平均梯度和信息熵等图像像素，从而改善了图像的质量。
- 水下图像复原 /
- 暗原色先验 /
- 稀疏表示
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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参考文献(22)

YANG Aiping, ZHENG Jia, WANG Jian, et al. Underwater image restoration based on color cast removal and dark channel prior[J]. Journal of Electronics Information Technology, 2015, 37(11): 2541-2547. doi: 10.11999/JEIT 150483.

杨爱萍, 郑佳, 王建, 等. 基于颜色失真去除与暗通道先验的水下图像复原[J]. 电子与信息学报, 2015, 37(11): 2541-2547. doi: 10.11999/JEIT150483.

PADMAVATHI G, SUBASHINI P, MUTHU KUMAR M, et al. Comparison of filters used for underwater image pre-processing[J]. International Journal of Computer Science Network Security, 2010, 10(1): 58-65.

郭相凤, 贾建芳, 杨瑞峰, 等. 基于水下图像光学成像模型的清晰化算法[J]. 计算机应用, 2012, 32(10): 2836-2839.

GUO Xiangfeng, JIA Jianfang, YANG Ruifeng, et al. Visibility enhancing algorithm based on optical imaging model for underwater images[J]. Journal of Computer Applications, 2012, 32(10): 2836-2839. doi: 10.3724/SP.J. 1087.2012.02836.

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.

曹美, 盛惠兴, 李庆武, 等. 基于暗原色先验模型的水下彩色图像增强算法[J]. 量子电子学报, 2016, 33(2): 140-147.

CAO Mei, SHENG Huixing, LI Qingwu, CHENG Yaling, et al. Underwater color image enhancement algorithm based on prior dark-channel modelJ]. Chinese Journal of Quantum Electronics, 2016, 33(2): 140-147.

ELAD Michael and AHARON Michal. Image denosing via sparse and redundant representations over learned dictionaries[J]. IEEE Transactions on Image Processing, 2006, 15(12): 3736-3745.

WANG Xin, SHEN Siqiu, NING Chen, et al. A sparse representation-based method for infrared dim target detection under seasky background[J]. Infrared Physics Technology, 2015, 71: 347-355. doi: 10.1016/j.infrared.2015. 05.014.

GUHA T and WARD R K. Learning sparse representations for human action recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(8): 1576-1588. doi: 10.1109/TPAMI.2011.253.

WANG Xin, SHEN Siqiu, NING Chen, et al. Multi-class remote sensing object recognition based on discriminative sparse representation[J]. Applied Optics, 2016, 55(6): 1381-1394. doi: 10.1364/AO.55.001381.

李建. 水下图像后向散射噪声的去噪问题研究[D]. [硕士论文] 中国海洋大学, 2009.

戴昊. 基于小波的水下图像去噪研究[D]. [硕士论文], 中国海洋大学, 2008.

HE Kaiming, SUN Jian, and TANG Xiaoou. Guided image filtering[C]. European Conference on Computer Vision, Heraklion, Crete, Greece, 2010: 1-14. doi: 10.1007/978-3-　642-15549-9_1.

TROPP J A and GILBERT A C. Signal recovery from random measurements via orthogonal matching pursuit[J]. IEEE Transactions on Information Theory, 2007, 53(12): 4655-4666. doi: 10.1109/TIT.2007.909108.

AHARON M, ELAD M, and BRUCKSTEIN A. K-svd: An algorithm for designing overcomplete dictionaries for sparse representation[J]. IEEE Transactions on Image Processing, 2006, 54(11): 4311-4322.

ZHANG Jian, ZHAO Debin, and GAO Wen. Group-based sparse representation for image restoration[J]. IEEE Transactions on Image Processing, 2014, 23(8): 3336-3351.

RUDIN L I, OSHER S, and FATEMI E. Nonlinear total variation based noise removal algorithms[C]. Eleventh International Conference of the Center for Nonlinear Studies on Experimental Mathematics: Computational Issues in Nonlinear Science: Computational Issues in Nonlinear Science, Los Alamos, New Mexico, USA, 1992: 259-268. doi: 10.1016/0167-2789(92)90242-F.

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

WANG Xin and TANG Zhenmin. Automatic image de-weathering using physical model and maximum entropy[C]. IEEE Conference on Cybernetics Intelligent System, Chengdu, China, 2008: 996-1001.

ZHANG Lin, ZHANG Lei, MOU Xuanqin, et al. FSIM: A feature similarity index for image quality assessment[J]. IEEE Transactions on Image Processing, 2011, 20(8): 2378-2386.

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