利用加权预测的图像迭代盲解卷积

邸男; 付东辉; 王毅楠

Volume 4 Issue 5

Oct. 2011

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Chinese Optics > 2011 > 4(5): 514-518.

DI Nan, FU Dong-hui, WANG Yi-nan. Iterative blind deconvolution of image with weighted prediction[J]. Chinese Optics, 2011, 4(5): 514-518.

Citation:

DI Nan, FU Dong-hui, WANG Yi-nan. Iterative blind deconvolution of image with weighted prediction[J]. Chinese Optics, 2011, 4(5): 514-518.

DI Nan, FU Dong-hui, WANG Yi-nan. Iterative blind deconvolution of image with weighted prediction[J]. Chinese Optics, 2011, 4(5): 514-518.

Citation:

DI Nan, FU Dong-hui, WANG Yi-nan. Iterative blind deconvolution of image with weighted prediction[J]. Chinese Optics, 2011, 4(5): 514-518.

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Iterative blind deconvolution of image with weighted prediction

1.
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China;
2.
Changchun Railway Vehicles Co., Ltd., Changchun 130062, China

Received Date: 11 Jul 2011
Rev Recd Date: 13 Aug 2011
Publish Date: 25 Oct 2011

Abstract

Abstract

To modify the image degradation caused by atmospheric turbulence, this paper proposes a new algorithm based on the iterative blind deconvolution with weighted prediction to solve the problems on instability convergence and huge complexity from traditional blind deconvolution methods. By optimizing an existing iterative blind deconvolution L-R algorithm, the proposed algorithm uses the weighting to obtain prediced values at the end of every iterative step, it then calculates the acceleration operators according to the predicted values to improve its convergence speed. Experiments show that the algorithm is capable of restoring the turbulence degraded image and the convergence have speeded about 43.8 times as compared with that of L-R algorithm. The algorithm's fast convergence shows its great practical value.
- iterative blind deconvolution,
- weighted prediction,
- L-R algorithm

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References(1)

References

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