A Random Field Computational Adaptive Optics Framework for Optical Coherence Microscopy

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.