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Proceedings Paper

Adaptive defocus blurred image restoration based on fractional Fourier transform combining with clarity-evaluation-function
Author(s): Li Zhang; Zhenming Peng
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Paper 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.

Paper Details

Date Published: 30 October 2009
PDF: 6 pages
Proc. SPIE 7495, MIPPR 2009: Automatic Target Recognition and Image Analysis, 74954L (30 October 2009); doi: 10.1117/12.830827
Show Author Affiliations
Li Zhang, Univ. of Electronic Science and Technology of China (China)
Zhenming Peng, Univ. of Electronic Science and Technology of China (China)


Published in SPIE Proceedings Vol. 7495:
MIPPR 2009: Automatic Target Recognition and Image Analysis
Tianxu Zhang; Bruce Hirsch; Zhiguo Cao; Hanqing Lu, Editor(s)

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