13 May 2013 Non-Bayesian noise reduction in digital holography by random resampling masks
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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.
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Vittorio Bianco, Melania Paturzo, Pasquale Memmolo, Andrea Finizio, Bahram Javidi, Pietro Ferraro, "Non-Bayesian noise reduction in digital holography by random resampling masks", Proc. SPIE 8788, Optical Measurement Systems for Industrial Inspection VIII, 878831 (13 May 2013); doi: 10.1117/12.2020865; https://doi.org/10.1117/12.2020865
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