Underwater image restoration algorithm to restrain correlated noise

被引:0
作者
Xiao, Yihan [1 ]
Pang, Yongjie [2 ]
Zhao, Lanfei [1 ]
机构
[1] College of Information and Communication Engineering, Harbin Engineering University, Harbin
[2] Science and Technology on Underwater Vehicle Laboratory, Harbin Engineering University, Harbin
来源
Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University | 2015年 / 36卷 / 06期
关键词
Correlated noise; Gradient method; Underwater image; Variational model;
D O I
10.3969/j.issn.1006-7043.201404045
中图分类号
学科分类号
摘要
The traditional algorithm cannot get rid of the influence of correlated noise on underwater images. In view of this, this paper proposes a restoration algorithm to restrain the correlated noises of underwater images by establishing a variational model based on the theory of maximum a posteriori estimation that represents convex property. In the variational model, the probability distribution of noise is guaranteed via the item of Bayesian restraint. Meanwhile the property of spatial continuity is satisfied via the Markov constraint. The gradient method is employed to obtain the optimal solution of the variational model, i.e. the restoration image without correlated noise. Experimental results show the correlated noise is eliminated by the proposed algorithm effectively; the restoration images present an excellent visual effect. ©, 2015, Editorial Board of Journal of HEU. All right reserved.
引用
收藏
页码:841 / 846
页数:5
相关论文
共 21 条
  • [1] Zhang W., Yang K., Fan F., Et al., Blind deconvolution approach based on blur metric method for laser underwater image restoration, Optics and Optoelectronic Technology, 9, 2, pp. 27-32, (2011)
  • [2] Chang Y., Xie Z., Peng F., Et al., Filtering algorithm on underwater laser image stained by speckle noise, Infrared and Laser Engineering, 31, 4, pp. 318-321, (2002)
  • [3] Buades A., Coll B., Morel J.M., A review of image denoising algorithms with a new one, SIAM Journal on Multiscale Modeling and Simulation, 4, 2, pp. 490-530, (2006)
  • [4] Wang X., Wrap-around effect removal finite ridgelet transform for multiscale image denoising, Pattern Recognition, 43, 11, pp. 3963-3968, (2010)
  • [5] Zhang M., Gunturk B.K., Multiresolution bilateral filtering for image denoising, IEEE Transactions on Image Processing, 17, 12, pp. 2324-2333, (2008)
  • [6] Han C., Guo H., Wang C., Et al., A novel method to reduce speckle in SAR images, International Journal of Remote Sensing, 23, 23, pp. 5095-5101, (2002)
  • [7] Repetti A., Chouzenoux E., Pesquet J.C., A penalized weighted least squares approach for restoring data corrupted with signal-dependent noise, Proceedings of 20th European Signal Processing Conference, pp. 1553-1557, (2012)
  • [8] Kofidis E., Theodoridis S., Kotropoulos C., Et al., Nonlinear adaptive filter for speckle suppression in ultrasonic images, Signal Processing, 52, 3, pp. 357-372, (1996)
  • [9] Foi A., Katkovnik V., Egiazarian K., Pointwise shape-adaptive DCT for high-quality denoising and deblocking of grayscale and color images, IEEE Transactions on Image Processing, 16, 5, pp. 1395-1411, (2007)
  • [10] Buades A., Coll B., Morel J.M., A review of image denoising algorithms, with a new one, SIAM Journal on Multiscale Modeling and Simulation, 4, 2, pp. 490-530, (2006)