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

arXiv 2306.11750 ↗First posted 19 Jun 2023eess.IV cs.CVcited by 0

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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.

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.

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References · 16

  1. 2016Tensor Ring Decompositioncited by 127
  2. 2016Efficient tensor completion for color image and video recovery: Low-rank tensor traincited by 61
  3. 2017Efficient Low Rank Tensor Ring Completioncited by 45
  4. 2018Tensor Ring Decomposition with Rank Minimization on Latent Space: An Efficient Approach for Tensor Completioncited by 42
  5. 2015Smooth PARAFAC Decomposition for Tensor Completioncited by 34
  6. 2014Efficient Nonnegative Tucker Decompositions: Algorithms and Uniquenesscited by 17
  7. 2018Missing Slice Recovery for Tensors Using a Low-rank Model in Embedded Spacecited by 17
  8. 2021Provable Tensor-Train Format Tensor Completion by Riemannian Optimizationcited by 17
  9. 2018Deep Learning based Retinal OCT Segmentationcited by 16
  10. 2018High-dimension Tensor Completion via Gradient-based Optimization Under Tensor-train Formatcited by 12
  11. 2019Tensor-Ring Nuclear Norm Minimization and Application for Visual Data Completioncited by 12
  12. 2020Block Hankel Tensor ARIMA for Multiple Short Time Series Forecastingcited by 12
4 more
  1. 2021TedNet: A Pytorch Toolkit for Tensor Decomposition Networkscited by 9
  2. 2019Provable Tensor Ring Completioncited by 8
  3. 2020Tensor completion via nonconvex tensor ring rank minimization with guaranteed convergencecited by 3
  4. 2020Non-convex Super-resolution of OCT images via sparse representationcited by 1

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