Adaptive defocus blurred image restoration based on fractional Fourier transform combining with clarity-evaluation-function

Li Zhang; Zhenming Peng

doi:10.1117/12.830827

30 October 2009 Adaptive defocus blurred image restoration based on fractional Fourier transform combining with clarity-evaluation-function

Li Zhang, Zhenming Peng

Author Affiliations +

Proceedings Volume 7495, MIPPR 2009: Automatic Target Recognition and Image Analysis; 74954L (2009) https://doi.org/10.1117/12.830827
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China

Abstract

The key issue to restore the defocus blurred image is how to choose a degradation model of blurred image. Based on the fractional Fourier transform (FrFT) combining with the clarity-evaluation-function, we present an approach for the restoration of defocus blurred image. This method constructs a defocused imaging model based on FrFT and estimates the lost phase signals from the intensity signals by an iterative phase retrieval approach, in which the sharp restored image can be acquired by implementing inverse FrFT on complex image signal made from the estimated phase signals and intensity signals. Using this model combing with the clarity-evaluation-function, the FrFT order can be changed adaptively. Consequently, the sharper image can be obtained in the end. Experimental results show the effectiveness, the robustness, and the low complexity of this approach, which make it more suitable for real-time environment.

Citation Download Citation

Li Zhang and Zhenming Peng "Adaptive defocus blurred image restoration based on fractional Fourier transform combining with clarity-evaluation-function", Proc. SPIE 7495, MIPPR 2009: Automatic Target Recognition and Image Analysis, 74954L (30 October 2009); https://doi.org/10.1117/12.830827

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