THz-and-Sub-THz-Imaging-Toolbox

A toolbox for near-field THz imaging simulation and image reconstruction. This toolbox enables rapid development of near-field THz imaging radar systems by providing easy access to efficient simulation tools and diverse toolset with documented API. Additionally, the toolbox provides efficient generation of large near-field THz imaging scenarios useful for many data-driven applications.

• This toolbox includes algorithms for computing SAR images from irregular geometries. Demos for doing so can be found in the repository: Efficient 3-D Near-Field MIMO-SAR Imaging for Irregular Scanning Geometries, which follows our paper (arXiv, DOI) with the same title.
• To learn more about the reconstruction algorithms implemented in this repository, we suggest you consult the documentation in the companion repository Introduction-to-MIMO-FMCW-Radar.

Publication and Citation

If you appreciate our work, please cite one of the papers using this toolbox

• Deep Learning-Based Multiband Signal Fusion for 3-D SAR Super-Resolution (arXiv, DOI)
• Efficient CNN-based Super Resolution Algorithms for mmWave Mobile Radar Imaging (arXiv, DOI)
• A Vision Transformer Approach for Efficient Near-Field Irregular SAR Super-Resolution (arXiv, DOI)
• Efficient 3-D Near-Field MIMO-SAR Imaging for Irregular Scanning Geometries (arXiv, DOI)
• An FCNN-Based Super-Resolution mmWave Radar Framework for Contactless Musical Instrument Interface (arXiv, DOI)
• Improved Static Hand Gesture Classification on Deep Convolutional Neural Networks using Novel Sterile Training Technique (arXiv, DOI)
• Near-Field MIMO-ISAR Millimeter-Wave Imaging (arXiv, DOI)

@article{smith2023deep,
	title        = {Deep Learning-Based Multiband Signal Fusion for 3-D SAR Super-Resolution},
	author       = {Smith, J. W. and Torlak, M.},
	year         = 2023,
	month        = apr,
	journal      = {IEEE Trans. Aerosp. Electron. Syst.},
	pages        = {1--17}
}
@inproceedings{smith2022vision,
	title        = {A Vision Transformer Approach for Efficient Near-Field {SAR} Super-Resolution under Array Perturbation},
	author       = {Smith, J. W. and Alimam, Y. and Vedula, G. and Torlak, M.}
	year         = 2022,
	month        = apr,
	booktitle    = {Proc. IEEE Tex. Symp. Wirel. Microw. Circuits Syst. (WMCS)},
	address      = {Waco, TX, USA},
	pages        = {1--6}
}
@article{smith2022efficient,
	title        = {Efficient {3-D} Near-Field {MIMO-SAR} Imaging for Irregular Scanning Geometries},
	author       = {Smith, J. W. and Torlak, M.},
	year         = 2022,
	month        = jan,
	journal      = {IEEE Access},
	volume       = 10,
	pages        = {10283--10294}
}
@inproceedings{vasileiou2022efficient,
	title        = {Efficient {CNN}-Based Super Resolution Algorithms for {mmWave} Mobile Radar Imaging},
	author       = {Vasileiou, C. and Smith, J. W. and Thiagarajan, S. and Nigh, M. and Makris, Y. and Torlak, M.},
	year         = 2022,
	month        = oct,
	booktitle    = {Proc. IEEE Int. Conf. Image Process. (ICIP)},
	address      = {Bordeaux, France},
	pages        = {3803--3807}
}
@article{smith2021fcnn,
	title        = {An {FCNN}-Based Super-Resolution {mmWave} Radar Framework for Contactless Musical Instrument Interface},
	author       = {Smith, J. W. and Furxhi, O. and Torlak, M.},
	year         = 2021,
	month        = may,
	journal      = {IEEE Trans. Multimedia},
	volume       = 24,
	pages        = {2315--2328}
}
@inproceedings{smith2020near,
	title        = {Near-Field {MIMO-ISAR} Millimeter-Wave Imaging},
	author       = {Smith, J. W. and Yanik, M. E. and Torlak, M.},
	year         = 2020,
	month        = sep,
	booktitle    = {Proc. IEEE Radar Conf. (RadarConf)},
	address      = {Florance, Italy},
	pages        = {1--6}
}
@article{smith2021improved,
	title        = {Improved Static Hand Gesture Classification on Deep Convolutional Neural Networks Using Novel Sterile Training Technique},
	author       = {Smith, J. W. and Thiagarajan, S. and Willis, R. and Makris, Y. and Torlak, M.},
	year         = 2021,
	month        = jan,
	journal      = {IEEE Access},
	volume       = 9,
	pages        = {10893--10902}
}
@article{smith2023terahertz,
   author = {Smith, J. W. and Torlak, M.},
   title = {Terhertz Imaging Toolbox with Interactive User Interface},
   journal = {},
   volume = {},
   number = {},
   pages = {},
   year = {2023},
}

For more information, visit the WISLAB website.

Installation

• Fork the GitHub repository or download from MATLAB
• Use directly from the downloaded files
• OR, install to MATLAB using the .mltbx installer
• Call "THzImagingToolbox" or "THzImagingGUI" in MATLAB

Usage

• See docs/THz_Toolbox_Instructions.pdf for more details
• See the documentation in MATLAB's documentation explorer

License

GPL 3.0