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 \({\upmu }\)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.
The authors would like to thank Natural Science and Engineering Research Counsel of Canada, Canadian Institutes of Health Research, and the Canada Research Chairs program.
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Boroomand, A., Tan, B., Shafiee, M.J., Bizheva, K., Wong, A. (2019). A Random Field Computational Adaptive Optics Framework for Optical Coherence Microscopy. In: Karray, F., Campilho, A., Yu, A. (eds) Image Analysis and Recognition. ICIAR 2019. Lecture Notes in Computer Science(), vol 11663. Springer, Cham. https://doi.org/10.1007/978-3-030-27272-2_24
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