Optical Coherence Tomography Image Enhancement via Block Hankelization
  and Low Rank Tensor Network Approximation
Farnaz Sedighin Andrzej Cichocki Hossein Rabbani
In this paper, the problem of image super-resolution for Optical Coherence
Tomography (OCT) has been addressed. Due to the motion artifacts, OCT imaging
is usually done with a low sampling rate and the resulting images are often
noisy and have low resolution. Therefore, reconstruction of high resolution OCT
images from the low resolution versions is an essential step for better OCT
based diagnosis. In this paper, we propose a novel OCT super-resolution
technique using Tensor Ring decomposition in the embedded space. A new
tensorization method based on a block Hankelization approach with overlapped
patches, called overlapped patch Hankelization, has been proposed which allows
us to employ Tensor Ring decomposition. The Hankelization method enables us to
better exploit the inter connection of pixels and consequently achieve better
super-resolution of images. The low resolution image was first patch Hankelized
and then its Tensor Ring decomposition with rank incremental has been computed.
Simulation results confirm that the proposed approach is effective in OCT
super-resolution.