Computer Science – Computer Vision and Pattern Recognition
Scientific paper
2011-08-15
Computer Science
Computer Vision and Pattern Recognition
Submitted to the 10th IMEKO Symposium LMPMI (Laser Metrology for Precision Measurement and Inspection in Industry) on May 31,
Scientific paper
Sparse modeling is one of the efficient techniques for imaging that allows recovering lost information. In this paper, we present a novel iterative phase-retrieval algorithm using a sparse representation of the object amplitude and phase. The algorithm is derived in terms of a constrained maximum likelihood, where the wave field reconstruction is performed using a number of noisy intensity-only observations with a zero-mean additive Gaussian noise. The developed algorithm enables the optimal solution for the object wave field reconstruction. Our goal is an improvement of the reconstruction quality with respect to the conventional algorithms. Sparse regularization results in advanced reconstruction accuracy, and numerical simulations demonstrate significant enhancement of imaging.
Astola Jaakko
Katkovnik Vladimir
Migukin Artem
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