# Optical Coherence Tomography Image Enhancement via Block Hankelization and Low Rank Tensor Network Approximation

arXiv: https://arxiv.org/abs/2306.11750 · HTML: https://mlsanity.com/paper/2306.11750/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 76 references that are in the map.
- First posted on arXiv 19 Jun 2023.

## Builds on (most cited first, 16 of 16)

- [Tensor Ring Decomposition](https://mlsanity.com/paper/1606.05535/) (2016)
- [Efficient tensor completion for color image and video recovery: Low-rank tensor train](https://mlsanity.com/paper/1606.01500/) (2016)
- [Efficient Low Rank Tensor Ring Completion](https://mlsanity.com/paper/1707.08184/) (2017)
- [Tensor Ring Decomposition with Rank Minimization on Latent Space: An Efficient Approach for Tensor Completion](https://mlsanity.com/paper/1809.02288/) (2018)
- [Smooth PARAFAC Decomposition for Tensor Completion](https://mlsanity.com/paper/1505.06611/) (2015)
- [Efficient Nonnegative Tucker Decompositions: Algorithms and Uniqueness](https://mlsanity.com/paper/1404.4412/) (2014)
- [Missing Slice Recovery for Tensors Using a Low-rank Model in Embedded Space](https://mlsanity.com/paper/1804.01736/) (2018)
- [Provable Tensor-Train Format Tensor Completion by Riemannian Optimization](https://mlsanity.com/paper/2108.12163/) (2021)
- [Deep Learning based Retinal OCT Segmentation](https://mlsanity.com/paper/1801.09749/) (2018)
- [High-dimension Tensor Completion via Gradient-based Optimization Under Tensor-train Format](https://mlsanity.com/paper/1804.01983/) (2018)
- [Tensor-Ring Nuclear Norm Minimization and Application for Visual Data Completion](https://mlsanity.com/paper/1903.08888/) (2019)
- [Block Hankel Tensor ARIMA for Multiple Short Time Series Forecasting](https://mlsanity.com/paper/2002.12135/) (2020)
- [TedNet: A Pytorch Toolkit for Tensor Decomposition Networks](https://mlsanity.com/paper/2104.05018/) (2021)
- [Provable Tensor Ring Completion](https://mlsanity.com/paper/1903.03315/) (2019)
- [Tensor completion via nonconvex tensor ring rank minimization with guaranteed convergence](https://mlsanity.com/paper/2005.09674/) (2020)
- [Non-convex Super-resolution of OCT images via sparse representation](https://mlsanity.com/paper/2010.12576/) (2020)

## Abstract (arXiv)

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.