Automatic Digital Hologram Denoising by Spatiotemporal Analysis of Pixel-Wise Statistics

Year: 2013

Authors: Leo M., Distante C., Paturzo M., Memmolo P., Locatelli M., Pugliese E., Meucci R., Ferraro P.

Autors Affiliation: CNR—Istituto Nazionale di Ottica, 73010 Arnesano (LE), Italy; CNR—Istituto Nazionale di Ottica, Comprensorio “A. Olivetti,” I-80078 Pozzuoli (Naples), Italy; Center for Advanced Biomaterials for Health Care@CRIB, Istituto Italiano di Tecnologia, Napoli 80125, Italy; CNR—Istituto Nazionale di Ottica, I-50125 Firenze, Italy

Abstract: In this paper, a new technique to reduce the noise in a reconstructed hologram image is proposed. Unlike all the techniques in the literature, the proposed approach not only takes into account spatial information but also temporal statistics associated with the pixels. This innovative solution enables, at first, the automatic detection of the areas of the image containing the objects (foreground). This way, all the pixels not belonging to any objects are directly cleaned up and the contrast between objects and background is consistently increased. The remaining pixels are then processed with a spatio-temporal filtering which cancels out the effects of speckle noise, while preserving the structural details of the objects. The proposed approach has been compared with other common speckle denoising techniques and it is found to give better both visual and quantitative results.

Journal/Review: JOURNAL OF DISPLAY TECHNOLOGY

Volume: 9 (11)      Pages from: 904  to: 909

More Information: This work was supported by the Programma Operativo Nazionale (PON) project IT@CHA funded by the Italian Ministry of Education, University and Research (MIUR).
KeyWords: Automatic Detection; Innovative solutions; Quantitative result; Spatial informations; Spatio temporal filtering; Spatiotemporal analysis; Statistical learning; Structural details, Adaptive filtering; Holograms; Holography; Image denoising; Image segmentation; Speckle, Pixels
DOI: 10.1109/JDT.2013.2268936

ImpactFactor: 1.686
Citations: 9
data from “WEB OF SCIENCE” (of Thomson Reuters) are update at: 2024-11-24
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