title: Random resampling masks: a non-Bayesian one-shot strategy for noise reduction in digital holography.
abstract: Holographic imaging may become severely degraded by a mixture of speckle and incoherent additive noise. Bayesian approaches reduce the incoherent noise, but prior information is needed on the noise statistics. With no prior knowledge, one-shot reduction of noise is a highly desirable goal, as the recording process is simplified and made faster. Indeed, neither multiple acquisitions nor a complex setup are needed. So far, this result has been achieved at the cost of a deterministic resolution loss. Here we propose a fast non-Bayesian denoising method that avoids this trade-off by means of a numerical synthesis of a moving diffuser. In this way, only one single hologram is required as multiple uncorrelated reconstructions are provided by random complementary resampling masks. Experiments show a significant incoherent noise reduction, close to the theoretical improvement bound, resulting in image-contrast improvement. At the same time, we preserve the resolution of the unprocessed image.
authors: CNR-National Institute of Optics, Pozzuoli (NA), Italy. vittorio.bianco@ino.it
authors: V
authors: V
authors: Bianco
authors: M
authors: M
authors: Paturzo
authors: P
authors: P
authors: Memmolo
authors: A
authors: A
authors: Finizio
authors: P
authors: P
authors: Ferraro
authors: B
authors: B
authors: Javidi