Blind image restoration algorithm based on wavelet transform and NAS-RIF algorithm

被引：0
作者：
Key Laboratory of Optoelectronic Technology and Systems, Ministry of Education, Chongqing University, Chongqing 400044, China [1 ]
机构：
[1] Key Laboratory of Optoelectronic Technology and Systems, Ministry of Education, Chongqing University
来源：
Guangxue Xuebao | 2009年 / 11卷 / 3000-3003期
关键词：
Blind image restoration; Image processing; NAS-RIF algorithm; Regularization; Wavelet transform;
D O I：
10.3788/AOS20092911.3000
中图分类号：
学科分类号：
摘要：
An improved nonnegativity and support constraints recursive inverse filtering (NAS-RIF) algorithm based on wavelet transform is presented to restore blind images. The degraded image is decomposed to obtain its wavelet coefficients in wavelet domain. The image's different frequency sub-bands are also obtained. Then, NAS-RIF algorithm is used to restore degraded image in each sub-bands, different regularization terms are used in different sub-bands. By estimating the noise variance in each sub-bands, the adaptive regularization parameters can be calculated through the local properties of the observed image and the noise variance. The two simulating experiments are made and high signal to noise ratios (SNR) of 19.66 dB and 23.86 dB are obtained. The experimental results show that the method given by authors is more efficient than traditional space-adaptive regularization method.
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收藏
页码：3000 / 3003
页数：3
相关论文
共 11 条
  • [1] Zhang H., Luo D., Status and development of study on blind image restoration algorithm, J. Image and Graphics, 9, 10, pp. 1145-1152, (2004)
  • [2] Kunder D., Hatzinakos D., A novel blind deconvolution scheme for image restoration using recursive filtering, IEEE Transactions on Signal Processing, 46, 2, pp. 375-389, (1998)
  • [3] Chow T.W.S., Li X.D., Cho S.Y., Improved blind image restoration scheme using recurrent filtering, IEEE Proceedings-Vision Image Signal Process, 147, 1, pp. 23-28, (2000)
  • [4] Ong C.A., Chambers J.A., Enhanced NAS-RIF algorithm for blind image deconvolution, IEEE Trans. Image Processing, 8, 7, pp. 988-992, (1999)
  • [5] Jiang J., Deng Q., Zhang G., Regularization algorithm for blind image restoration based on wavelet transform, Optics and Precision Engineering, 15, 4, pp. 582-586, (2007)
  • [6] Zhao D., Zhou H., Image restoration algorithm of smoothing constrained least square based on wavelet denoising, Ordnance Industry Automation, 24, 5, pp. 82-84, (2005)
  • [7] Li S., Wang R., Li C., New method of image de-noising through wavelet shrinkage based on estimate of noise variance, Application Research of Computers, 24, 1, pp. 220-221, (2007)
  • [8] Deng S., Zeng S., Xu Z., A new method for selection of regularization parameter, Computer Engineering and Applications, 40, 16, pp. 50-52, (2004)
  • [9] Miao Q., Tang B., Zhou H., An effective image restoration algorithm based on regularization in spatial domain, Systems Engineering, 23, 11, pp. 91-94, (2005)
  • [10] Shi W., Yang Y., Wu Y., Et al., Numeric Analysis, (2002)
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