Endoscopic optical coherence tomography (OCT) is an emerging method for noninvasive microscopic probing in biomedicine. In this paper, the feasibility of alleviating the pixelated structural artifacts created by a fiber bundle-based OCT imaging method is investigated using a novel statistical analysis. We demonstrate an efficient nonparametric iterative compressive sensing (CS) technique that is efficient in reconstructing the original pattern shape from a pixelated image of a reference US Air Force resolution chart. An efficient implementation scheme for the shape recovery is presented along with the results of experiments that demonstrate a peak signal-to-noise ratio of 18 dB and a noise variation of less than 0.3 dB with no honeycomb effect in the image is obtained after 40 iterations which is significantly efficient than the previous iterative method of learning image priors.
Bibliographical noteFunding Information:
J. H. Han was supported in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF-2013R1A1A2062448) and partly supported by the ICT R&D program of MSIP/IITP [B0101-15-0307, Basic Software Research in Human-level Lifelong Machine Learning (Machine Learning Center)]. S. M. Yoon was supported by the ICT program of MSIP/IITP, Republic of Korea (#B0101-15-1347) and also supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2014R1A1A1002890). G.-J. Yoon was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2009-0093827). J.-H. Han and S. M. Yoon contributed equally in this work.
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- Biomedical imaging
- Compressive sensing
- Image reconstruction
- Optical coherence tomography
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Atomic and Molecular Physics, and Optics
- Electrical and Electronic Engineering