Abstract
A number of factors can degrade the resolution and contrast of OCT images, such as: (1) changes of the OCT pointspread
function (PSF) resulting from wavelength dependent scattering and absorption of light along the imaging depth
(2) speckle noise, as well as (3) motion artifacts. We propose a new Super Resolution OCT (SR OCT) imaging
framework that takes advantage of a Stochastically Fully Connected Conditional Random Field (SF-CRF) model to
generate a Super Resolved OCT (SR OCT) image of higher quality from a set of Low-Resolution OCT (LR OCT)
images. The proposed SF-CRF SR OCT imaging is able to simultaneously compensate for all of the factors mentioned
above, that degrade the OCT image quality, using a unified computational framework. The proposed SF-CRF SR OCT
imaging framework was tested on a set of simulated LR human retinal OCT images generated from a high resolution,
high contrast retinal image, and on a set of in-vivo, high resolution, high contrast rat retinal OCT images. The
reconstructed SR OCT images show considerably higher spatial resolution, less speckle noise and higher contrast
compared to other tested methods. Visual assessment of the results demonstrated the usefulness of the proposed
approach in better preservation of fine details and structures of the imaged sample, retaining biological tissue boundaries
while reducing speckle noise using a unified computational framework. Quantitative evaluation using both Contrast to
Noise Ratio (CNR) and Edge Preservation (EP) parameter also showed superior performance of the proposed SF-CRF
SR OCT approach compared to other image processing approaches.
