RICOTTA_Software

MULTI-FRAME SUPER-RESOLUTION MRI USING COUPLED LOW-RANK TUCKER APPROXIMATION

This software reproduces the results from the following:

@unpublished{prevost:hal-03617754,
  TITLE = ,
  AUTHOR = {Pr{\'e}vost, Cl{\'e}mence and ODILLE, F},
  URL = {https://hal.archives-ouvertes.fr/hal-03617754},
  NOTE = {working paper or preprint},
  YEAR = {2022},
  MONTH = Mar,
  PDF = {https://hal.archives-ouvertes.fr/hal-03617754/file/IRM_Tucker.pdf},
  HAL_ID = {hal-03617754},
  HAL_VERSION = {v1},
}

Link to the project

Content

/demos : contains demo files that produce tables and figures
/metrics : contains the metrics used to assess the quality of the reconstruction
/src : contains helpful files to run the demos

Minimal requirements

In order to run the demo file demo.m, you will need to:

Download and install Tensorlab 3.0: https://www.tensorlab.net
Download the Sharpness Index toolbox: http://www.mi.parisdescartes.fr/~moisan/sharpness/
Beltrami primal-dual solver: https://math.montana.edu/dzosso/code/

Please quote the corresponding papers if you decide to use these codes.

## How it works

### Generate coupled tensor model

In this software, we use the “MRI” dataset of MATLAB. The low-resolution observations are generated from the super-resolution image with manually-specified degradation matrices.

### Run algorithms

In reconstruction.m, we showcase the performance of three algorithms:

RICOTTA
Block-RICOTTA (applies RICOTTA to corresponding subblocks of the observations)
RICOTTA without regularization + 3D-Beltrami regularization (to highlight the importance of the Tikhonov regularization in the algorithm

The metrics and computation time are then displayed in a table. Slices of the reference and reconstructions are plotted in a figure.

Available demos

They are available in the /demos folder. The table below summarized what does what

Name	Content
`reconstruction.m`	Evaluates performance of the algorithms
`choice_ranks.m`	plots R-SNR, CC and RMSE as a function of the ranks
`choice_regul.m`	plots R-SNR, CC and RMSE as a function of the regul. parameter
`choice_weights.m`	plots R-SNR, CC and RMSE as a function of the weights lambda

This site is open source. Improve this page.