IMAGE RESTORATION AND ITS EFFECTIVENESS OF VARIOUS  BLIND IMAGE DECONVOLUTION ALGORITHMS

VISHWABRAHMA SHILPA   S.K. TYAGI

IMAGE RESTORATION AND ITS EFFECTIVENESS OF VARIOUS BLIND IMAGE DECONVOLUTION ALGORITHMS

Authors

VISHWABRAHMA SHILPA S.K. TYAGI

Keywords:

Image restoration, Blind deconvolution, Motion blur, Point-Spread-Function (PSF), Sparsity constraint, Maximum likelihood estimation

Abstract

Motion blur is an inevitable tradeoff between the amount of blur and the amount ofnoise in the acquired images. The effectiveness of any restoration algorithm typically dependson these amounts, and it is difficult to find their best balance in order to ease the restoration task. The Point-Spread-Function (PSF) trajectories

References

Ferreira, P. A., & Motta, R. R. (2000). Regularization techniques for blind image deconvolution. IEEE Transactions on Image Processing, 9(2), 295-301.

Levin, A., Weiss, Y., Durand, F., & Freeman, W. T. (2009). Understanding and evaluating blind deconvolution algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(12), 1-20.

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Published

2024-06-20

How to Cite

VISHWABRAHMA SHILPA S.K. TYAGI. (2024). IMAGE RESTORATION AND ITS EFFECTIVENESS OF VARIOUS BLIND IMAGE DECONVOLUTION ALGORITHMS. Journal of Computational Analysis and Applications (JoCAAA), 33(06), 4008–4015. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/5266