Paper Info
Title
A Random Field Computational Adaptive Optics Framework For Optical Coherence Microscopy
Abstract
A novel random field computational adaptive optics (R-CAO) framework is proposed to jointly correct for optical aberrations and speckle noise issues in optical coherence microscopy (OCM) and thus overcome the depth-of-field limitation in OCM imaging. The performance of the R-CAO approach is validated using OCM tomograms acquired from a standard USAF target and a phantom comprised of 1 mu m diameter microspheres embedded in agar gel. The R-CAO reconstructed OCM tomograms show reduced optical aberrations and speckle noise over the entire depth of imaging compared to the existing state-of-the-art computational adaptive optics algorithms such as the regularized maximum likelihood computational adaptive optics (RML-CAO) method. The reconstructed images using the proposed R-CAO framework show the usefulness of this method for the quality enhancement of OCM imaging over different imaging depths.
Year
DOI
Venue
2019
10.1007/978-3-030-27272-2_24
IMAGE ANALYSIS AND RECOGNITION (ICIAR 2019), PT II
Keywords
DocType
Volume
Computational adaptive optics, Optical coherence microscopy, Random field
Conference
11663
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Ameneh Boroomand100.34
Bingyao Tan200.34
M. J. Shafiee310022.85
Kostadinka K. Bizheva472.84
Alexander Wong535169.61