Featured Research

 

3D Augmented Reality Devices

Seeing through obscurations and automated object recognition with 3D augmented reality devices.

photo
  1. 2D imaging cannot see through obscurations
  2. 3D reconstructed image can see through partial obscurations
photo
 The 3D object visualized by the viewer with removed obscurations using an augmented reality device.
photo
 Typical augmented reality device in the experiments
For more details, see Adam Markman, Xin Shen, Hong Hua, and Bahram Javidi, “Augmented reality three-dimensional object visualization and recognition with axially distributed sensing,” Optics Letters 41, issue 2, pp 297-300 (January 2016)

3D Human Gesture and Activity Recognition
3-D Gesture Recognition Experiments

Images show the reconstruction capability of the system, for the same frame, and for a specific person and gesture:

  1. background,
  2. head, and
  3. fist
  4. Depth reconstruction focusing at the hand’s gesture open,
  5. Depth reconstruction focusing at the hand’s gesture left, and
  6. Depth reconstruction focusing at the hand’s gesture deny.

For details, see:

  1. Bahram Javidi, Filiberto Pla, José M. Sotoca, Xin Shen, Pedro Latorre-Carmona, Manuel Martínez-Corral, Rubén Fernández-Beltrán, and Gokul Krishnan, “Fundamentals of automated human gesture recognition using 3D integral imaging: a tutorial,” Advances in Optics and Photonics, 12, 1237-1299 (December 2020). [Top Download of AOP]

  2. Gokul Krishnan, Yinuo Huang, Rakesh Joshi, Timothy O’Connor, and Bahram Javidi, “Spatio-temporal continuous gesture recognition under degraded environments: performance comparison between 3D integral imaging (InIm) and RGB-D sensors,” Optics Express 29, 30937-30951 (September 2021).

  3. Gokul Krishnan, Rakesh Joshi, Timothy O’Connor, Filiberto Pla, and Bahram Javidi, “Human Gesture Recognition under Degraded Environments using 3D-Integral Imaging and Deep Learning,” Optics Express, 28, #13, 19711-19725 (June 22, 2020). [Top Download of Optics Express]

  4. Filiberto Pla, and Bahram Javidi, “Human gesture recognition using three-dimensional integral imaging,” The Journal of the Optical Society of America A, vol. 31, pp 2312-2320 (2014)

3D Displays
photo

Concept of 3D Displays using Integral Imaging

  1. Image capture stage
  2. 3D Display stage, which produces floating 3-D images in front of the monitor

For more information, please see:

  • Manuel Martinez-Corral and Bahram Javidi, “Fundamentals of 3D imaging and displays: A tutorial on integral imaging, Lightfield, and plenoptic systems,” Advances in Optics and Photonics, Vol. 10, issue 3, pp. 512-566, September 2018. [Top Download of AOP]
  • B. Javidi, A. Carnicer, J. Arai, T. Fujii, H. Hua, H. Liao, M. Martínez-corral, F. Pla, A. Stern, L. Waller, Q. H. Wang, G. Wetzstein, M. Yamaguchi, and H. Yamamoto, “Roadmap on 3D integral imaging: sensing, processing, and display,” Optics Express, 28(22), pp. 32266-32293 (October 2020). Top Download of Optics Express
photo

c. Two views of the 3-D image as displayed by the Integral Imaging 3D display

Source: Manuel Martínez-Corral, Adrián Dorado, Juan Carlos Barreiro, Genaro Saavedra, and Bahram Javidi, “Recent Advances in the Capture and Display of Macroscopic and Microscopic 3-D Scenes by Integral Imaging,” the Proceedings of IEEE Journal, Volume 105, Issue 5, 2017.

Manuel Martinez-Corral and Bahram Javidi, “Fundamentals of 3D imaging and displays: A tutorial on integral imaging, Lightfield, and plenoptic systems,” Advances in Optics and Photonics, Vol. 10, issue 3, pp. 512-566, September 2018. [Top Download of AOP].

B. Javidi, A. Carnicer, J. Arai, T. Fujii, H. Hua, H. Liao, M. Martínez-corral, F. Pla, A. Stern, L. Waller, Q. H. Wang, G. Wetzstein, M. Yamaguchi, and H. Yamamoto, “Roadmap on 3D integral imaging: sensing, processing, and display,” Optics Express, 28(22), pp. 32266-32293 (October 2020). Top Download of Optics Express

3D Object Tracking from behind Obscurations

photo
  1. scene capture process and
  2. 3D reconstruction of the scene
photo
 3. Segmented and tracked objects from behind occlusion

  • Pranav Wani, Kashif Usmani, Gokul Krishnan, and Bahram Javidi, “3D object tracking using integral imaging with mutual information and Bayesian optimization,” Optics Express 32, 7495-7512 (15 February 2024).
  • Y. Zhao, X. Xiao, M. Cho, and B. Javidi, ”Tracking of Multiple Objects in Unknown Background using Bayesian Estimation in 3D Space,” the Journal of Optical Society of America A, Vol. 28, No. 9, pp 1935-1940, September 2011.

Long Range 3D Imaging for Defense and Security Applications

a. Tests conducted from the US Air Force Sensors Directorate tower and camera railb. Results of the obscuration penetration experiment

Source: Daniel LeMaster, Barry Karch, and Bahram Javidi, “Mid-Wave Infrared 3D Integral Imaging at Long Range,” IEEE Journal of Display Technology, vol. 9, pp. 545-551 (1 July 2013)

Multidimensional Optical Sensing and Imaging System (MOSIS)

photo

MOSIS 2.0 for 3-D object shape and material inspection experiments in the presence of occlusion. Captured and computed multidimensional elemental images.

  1. Visible spectrum and
  2. NIR spectrum.
  3. Degree of polarization (DoP) computed by the Stokes parameters and
  4. depth map by the estimation method
  • Source: Bahram Javidi, Xin Shen, Adam S. Markman, Pedro Latorre-Carmona, Adolfo Martínez-Uso, José Martinez Sotoca, Filiberto Pla, Manuel Martínez-Corral, Genaro Saavedra, Yi-Pai Huang, Adrian Stern, “Multidimensional Optical Sensing and Imaging Systems (MOSIS): From Macro to Micro Scales,” the Proceedings of the IEEE Journal, Volume 105, Issue 5, 2017.
  • Gokul Krishnan, Jiheon Lee, Saurabh Goswami, and Bahram Javidi, “Physics informed image restoration under low illumination with simultaneous parameter estimation using 3D integral imaging and Bayesian neural networks,” Optics Express 33, 6121-6134 (Feb 2025).
  • P. Wani, K. Usmani, G. Krishnan and B. Javidi, “Assessment of 3D Integral Imaging Information Loss in Degraded Environments,” IEEE Access, vol. 12, pp. 166643-166651, November 2024, doi: 10.1109/ACCESS.2024.3493601.
  • Gokul Krishnan, Saurabh Goswami, Rakesh Joshi, and Bahram Javidi, “Three-dimensional integral imaging-based image descattering and recovery using physics informed unsupervised CycleGAN,” Optics Express 32, 1825-1835 (Jan. 2024).
  • Pranav Wani, Kashif Usmani, and Bahram Javidi, “3D integral imaging depth estimation of partially occluded objects using mutual information and Bayesian optimization,” Optics Express, 31, (July 2023). doi.org/10.1364/OE.492160
photo

Diagram of the Multidimensional Optical Sensing and Imaging System (MOSIS)

Hardware Security and Authentication of Integrated Circuits using Micro/Nano Optical ID Tags
photo

(a) 449×641 pixel binary image.

(b) 3.15 mm x 3.15 mm QR code storing the encrypted and compressed image shown in (a) placed on a 14.5 mm x 52.1 mm IC; an image of the QR code placed next to a dime is also depicted
photo

Enlarged QR code taken using the iPhone 4 camera; and scanned QR code depicting the encrypted and compressed data using the iPhone SCAN Application

  • Source: A. Markman, B. Javidi, and M. Tehranipoor, “Photon-Counting Security Tagging and Verification Using Optically Encoded QR Codes,” (IEEE) Photonics Journal, vol. 6, issue 1, 2014.
  • Youhyun Kim, Ongee Jeong, Inkyu Moon, and Bahram Javidi, “Multiparty Random Phase Wrapping Secret-Sharing Systems for Visual Data Security, IEEE Transactions on Systems, Man, and Cybernetics, Volume 55, Issue 5, pp. 3586-3600, May 2025, DOI: 10.1109/TSMC.2025.3541827

A. Carnicer, B. Javidi, “Authentication of gold nanoparticle encoded pharmaceutical tablets using polarimetric signatures,” Optics Letters, Vol. 41, Issue 19, pp. 4507-4510 (October 2016).

Adam Markman, Artur Carnicer, and Bahram Javidi, “Security authentication with a three-dimensional optical phase code using random forest classifier,” Journal of Optical Society of America A 33, 1160-1165 (June 2016)

Bio Photonics, 3D Microscopy, and Automated Disease Identification
Schematics of an experimental setup to measure the complex amplitude of red blood cells (RBCs) to capture opto-biological signatures
bio photonics microscopy machine

(a) Optical configuration and (b) 3D-printed experimental system with dimensions of 94 mm x 107 mm x 190.5 mm used in RBC data collection.

(a), (d) Shearing interferograms.

(b), (e) RBC gradient phase image retrieved from shearing interferograms in (a) and (d), respectively.

(c), (f) RBC gradient amplitude image retrieved from shearing interferograms in (a) and (d) respectively.

(a)–(c) Are from a healthy RBC, whereas (d)–(f) are from a malaria infected RBC

Source:

  1. Timothy O’Connor and Bahram Javidi, “COVID-19 screening with digital holographic microscopy using intra-patient probability functions of spatio-temporal bio-optical attributes,” Biomedical Optics Express 13, 5377-5389 (September 2022). [Highlighted by Optica (OSA) as an Editor’s Pick]

  2. Peter M. Douglass, Timothy O’Connor, and Bahram Javidi, “Automated sickle cell disease identification in human red blood cells using a lensless single random phase encoding biosensor and convolutional neural networks,” Optics Express 30, 35965-35977 (September 2022).

  3. Timothy O’Connor, Sabato Santaniello, and Bahram Javidi “COVID-19 detection from red blood cells using highly comparative time-series analysis (HCTSA) in digital holographic microscopy,” Optics Express, 30, 1723-1736, January 2022.

  4. Timothy O’Connor, Jian-Bing Shen, Bruce T. Liang, and Bahram Javidi, “Digital holographic deep learning of red blood cells for field-portable, rapid COVID-19 screening,” Optics Letters 46, 2344-2347 (May 2021). [Selected for Spotlight in Optics] and [Top Download]

 

Security, Anti-Counterfeiting, and Authentication using Nano Technologies
photo
  1. AuNP (gold) QR code (sample A),
  2. platinum QR code (sample B), and
  3. AuNP (gold) structure without QR code (sample C)
photo
 Optical setup for authentication of codes produced with gold nanoparticles
photo

Principal component analysis of the histograms. Red dots, sample A; green dots, sample B; blue dots, sample C

Sources: A. Carnicer, A. Hassanfiroozi, P. Latorre-Carmona, Y.-P. Huang, and B. Javidi, “Security authentication using phase-encoded nanoparticle structures and polarized light,” Opt. Lett. 40, 135–138 (2015); and Artur Carnicer and Bahram Javidi, “Optical Security and Authentication using Nanoscale and Thin Film structures,” Advances in Optics and Photonics, vol 9, issue 2, pp 218-256, June 30, 2017

Underwater Sensing Consisting of Underwater Vehicles (AUV)
Figure (a) An example of underwater sensing consisting of underwater vehicles (AUV) communicating with each other. (b) Integral Imaging sensing. (c) Diagram of integral imaging experimental setup to capture video sequences of the optical signal sent by the light source (blue LED).

  1. Yinuo Huang, Gokul Krishnan, Saurabh Goswami, and Bahram Javidi, “Underwater optical signal detection system using diffuser-based lensless imaging,” Optics Express 32, 1489-1500 (Jan. 2024).

  2. Gregory Aschenbrenner, Yinuo Huang, Rakesh Joshi, Bahram Javidi, “High-speed Temporal Optical Signal Detection in Turbid Media using Lensless Single Random Phase Encoding, Optics and Lasers in Engineering, Volume 188, May 2025. https://doi.org/10.1016/j.optlaseng.2025.108911

  3. Rakesh Joshi, Jiheon Lee, and Bahram Javidi, “High-speed 3D integral imaging for sensing and visualization of dynamic underwater events,” Optics Continuum 3, 1498-1508 (August 2024).

  4. Rakesh Joshi, Kashif Usmani, Gokul Krishnan, Fletcher Blackmon, and Bahram Javidi, “Underwater object detection and temporal signal detection in turbid water using 3D-integral imaging and deep learning,” Opt. Express 32, 1789-1801 (2024).