Images from coherent laser sources are severely degraded by a mixture of speckle and incoherent additive noise. In digital holography, Bayesian approaches reduce the incoherent noise, but prior information are needed about the noise statistics. On the other hand, non-Bayesian techniques presents the shortcomings of resolution loss or very complex acquisition systems, required to record multiple uncorrelated holograms to be averaged. Here we propose a fast non-Bayesian method which performs a numerical synthesis of a moving diffuser in order to reduce the noise. The method does not depend on prior knowledge of the noise statistics and the proposed technique is one-shot, as only one single hologram capture is required. Indeed, starting from a single acquisition multiple uncorrelated reconstructions are provided by random sparse resampling masks, which can be incoherently averaged. Experiments show a significant improvement, close to the theoretical bound. Noteworthy, this is achieved while preserving the resolution of the unprocessed image.

Non-Bayesian noise reduction in digital holography by random resampling masks

Bianco Vittorio;Paturzo Melania;Memmolo Pasquale;Finizio Andrea;Ferraro Pietro
2013

Abstract

Images from coherent laser sources are severely degraded by a mixture of speckle and incoherent additive noise. In digital holography, Bayesian approaches reduce the incoherent noise, but prior information are needed about the noise statistics. On the other hand, non-Bayesian techniques presents the shortcomings of resolution loss or very complex acquisition systems, required to record multiple uncorrelated holograms to be averaged. Here we propose a fast non-Bayesian method which performs a numerical synthesis of a moving diffuser in order to reduce the noise. The method does not depend on prior knowledge of the noise statistics and the proposed technique is one-shot, as only one single hologram capture is required. Indeed, starting from a single acquisition multiple uncorrelated reconstructions are provided by random sparse resampling masks, which can be incoherently averaged. Experiments show a significant improvement, close to the theoretical bound. Noteworthy, this is achieved while preserving the resolution of the unprocessed image.
2013
Istituto di Scienze Applicate e Sistemi Intelligenti "Eduardo Caianiello" - ISASI
Istituto Nazionale di Ottica - INO
978-0-8194-9604-1
Digital Holography
Imaging
Noise reduction
Speckle
Image processing
random masks
multiple holograms
non-Bayesian estimation
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/257876
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