Prof. Steven L. Brunton Profile

Prof. Steven L. Brunton

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Publications (5)

Proceedings Article | 3 October 2022 Paper

Wavefront sensor fusion via shallow decoder neural networks for aero-optical predictive control

Shervin Sahba, Christopher Wilcox, Austin McDaniel, Benjamin Shaffer, Steven Brunton, J. Nathan Kutz

Proceedings Volume 12223, 1222303 (2022) https://doi.org/10.1117/12.2631951

KEYWORDS: Sensors, Wavefront sensors, Sensor fusion, Adaptive optics, Neural networks, Wavefronts, Control systems, Data modeling, Optical turbulence, Aerodynamics

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SPIE Journal Paper | 27 January 2022

Dynamic mode decomposition for aero-optic wavefront characterization

Shervin Sahba, Diya Sashidhar, Christopher Wilcox, Austin McDaniel, Steven Brunton, J. Nathan Kutz

OE, Vol. 61, Issue 01, 013105, (January 2022) https://doi.org/10.1117/12.10.1117/1.OE.61.1.013105

KEYWORDS: Digital micromirror devices, Wavefronts, Adaptive optics, Wave propagation, Optical engineering, Sensors, Laser beam propagation, Refraction, Near field optics, Data modeling

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SPIE Journal Paper | 29 September 2021

Intensity-Mosaic: automatic panorama mosaicking of disordered images with insufficient features

Chen Gong, Steven Brunton, Brian Schowengerdt, Eric Seibel

JMI, Vol. 8, Issue 05, 054002, (September 2021) https://doi.org/10.1117/12.10.1117/1.JMI.8.5.054002

KEYWORDS: Panoramic photography, Image registration, Retina, Video, Dimension reduction, Bladder, Principal component analysis, Video surveillance, Microscopes, Image processing

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Purpose: Handling low-quality and few-feature medical images is a challenging task in automatic panorama mosaicking. Current mosaicking methods for disordered input images are based on feature point matching, whereas in this case intensity-based registration achieves better performance than feature-point registration methods. We propose a mosaicking method that enables the use of mutual information (MI) registration for mosaicking randomly ordered input images with insufficient features. Approach: Dimensionality reduction is used to map disordered input images into a low dimensional space. Based on the low dimensional representation, the image global correspondence can be recognized efficiently. For adjacent image pairs, we optimize the MI metric for registration. The panorama is then created after image blending. We demonstrate our method on relatively lower-cost handheld devices that acquire images from the retina in vivo, kidney ex vivo, and bladder phantom, all of which contain sparse features. Results: Our method is compared with three baselines: AutoStitch, “dimension reduction + SIFT,” and “MI-Only.” Our method compared to the first two feature-point based methods exhibits 1.25 (ex vivo microscope dataset) to two times (in vivo retina dataset) rate of mosaic completion, and MI-Only has the lowest complete rate among three datasets. When comparing the subsequent complete mosaics, our target registration errors can be 2.2 and 3.8 times reduced when using the microscopy and bladder phantom datasets. Conclusions: Using dimensional reduction increases the success rate of detecting adjacent images, which makes MI-based registration feasible and narrows the search range of MI optimization. To the best of our knowledge, this is the first mosaicking method that allows automatic stitching of disordered images with intensity-based alignment, which provides more robust and accurate results when there are insufficient features for classic mosaicking methods.

Proceedings Article | 11 August 2021 Presentation + Paper

Physics-informed machine-learning for modeling aero-optics

J. Nathan Kutz, Diya Sashidhar, Shervin Sahba, Steven Brunton, Austin McDaniel, Christopher Wilcox

Proceedings Volume 11817, 118170E (2021) https://doi.org/10.1117/12.2596540

KEYWORDS: Digital micromirror devices, Control systems, Data modeling, Wavefronts, Systems modeling, Machine learning, Transient nonlinear optics, Near field optics, Mathematical modeling, Algorithm development

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Proceedings Article | 11 March 2016 Paper

Self-tuning fiber lasers

Steven Brunton, J. Kutz, Xing Fu

Proceedings Volume 9728, 972830 (2016) https://doi.org/10.1117/12.2211773

KEYWORDS: Fiber lasers, Detection and tracking algorithms, Machine learning, Adaptive control, Mode locking, Nonlinear control, Control systems, Ultrafast lasers, Compressed sensing, Nonlinear optics, Birefringence, Wave plates, Polarizers

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