Qian Weixin, Wang Wanli, Qi Shuangxi, Chen Jinming, Liu Dongbing. Generalized variation-based regularization method for infrared image denoising[J]. Infrared and Laser Engineering, 2014, 43(1): 67-71.
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Qian Weixin, Wang Wanli, Qi Shuangxi, Chen Jinming, Liu Dongbing. Generalized variation-based regularization method for infrared image denoising[J]. Infrared and Laser Engineering, 2014, 43(1): 67-71.
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Generalized variation-based regularization method for infrared image denoising
- Received Date: 2013-05-10
- Rev Recd Date:
2013-06-13
- Publish Date:
2014-01-25
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Abstract
A generalized variation (GV) regularization based infrared image denoising method was proposed in this paper. In the new method, a p-norm was used as regularized term to replace total variation (TV) norm in traditional TV based image denoising methods which were used popular in image processing domain. Then a smoothing functional was constructed for noised removal. Thus, the problem of image denoising was transformed to a problem of a functional minimization. A nonlinear partial differential equation (PDE) was deduced from the new image denoising model. To solve the nonlinear PDE, the fixed point iteration (FPI) scheme was introduced to linear the PDE. The stability and convergence of regularized solution were ensured by FPI scheme. The numerical experimental results show that comparison with TV regularized method, the GV regularized method can preserve image edge including those small width edges more efficiently while removing noise. The GV regularized method is an efficient image noise removed method with better performance of noise removal and edge preserving.
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References
|
[1]
|
|
|
[2]
|
Chen Wei zhe n, Zhou Xiaodong, Zhang Chunhua. IR image enhancement algorithm based on analysis of detector noise characteristics [J]. Infrared and Laser Engineering, 2008, 37(S): 629-633. (in Chinese) |
|
[3]
|
|
|
[4]
|
Tikhonov A N. On solving incorrectly posed problem and method of regularization [J]. Dokl Acad Nauk USSR, 1963, 151(3): 501-504. |
|
[5]
|
|
|
[6]
|
Rudin L, Osher S, Fatemi E. Nonlinear total variation based noise removal algorithms[J]. Physica D, 1992, 60: 259-268. |
|
[7]
|
Vogel C R, Oman M E. Fast, robust total variation-based reconstruction of noisy, blurred images[J]. IEEE Transactions on Image Processing, 1998, 7(6): 813-824. |
|
[8]
|
|
|
[9]
|
Chan T F, Wong C K. Total variation blind deconvolution [J]. IEEE Transactions on ImageProcessing, 1998, 7(3): 370-375. |
|
[10]
|
|
|
[11]
|
|
|
[12]
|
Chan T F, Osher S, Shen J. The digital TV filter and nonlinear denoising [J]. IEEE Trans on Image Processing, 2001, 10(2): 231-241. |
|
[13]
|
Acar R, Vogel C R. Analysis of bounded variation penalty methods for ill-posed problem [J]. Inverse Problem, 1994, 10: 1217-1229. |
|
[14]
|
|
|
[15]
|
Vogel C R, Oman M E. Iterative methods for total variation denoising[J]. SIAM J Sci Comput, 1996, 17(1): 227-238. |
|
[16]
|
|
|
[17]
|
Liu Yongjin, Zhang Guohua, Zhao Yigong. Application of digital total variation filter to nonuniformity correction in infrared image sequences[J]. Infrared and Laser Engineering, 2012, 41(8): 2216-2221. (in Chinese) |
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