This guest editorial summarizes the Special Section on Machine Learning in Optics

Axtell, Travis

Howe, Jonathan

Iftekharuddin, Khan

English

Old Dominion University

Old Dominion University ODU Digital Commons Electrical & Computer Engineering Faculty Publications Electrical & Computer Engineering 2020 Special Section Guest Editorial: Machine Learning In Optics Jonathan Howe Travis Axtell Khan Iftekharuddin Old Dominion University, kiftekha@odu.edu Follow this and additional works at: https://digitalcommons.odu.edu/ece_fac_pubs  Part of the Artificial Intelligence and Robotics Commons, Electrical and Computer Engineering Commons, and the Optics Commons Original Publication Citation Howe, J., Axtell, T., & Iftekharuddin, K. (2020). Special section guest editorial: Machine learning in optics. Optical Engineering, 59(5), 51401. doi:10.1117/1.OE.59.5.051401 This Editorial is brought to you for free and open access by the Electrical & Computer Engineering at ODU Digital Commons. It has been accepted for inclusion in Electrical & Computer Engineering Faculty Publications by an authorized administrator of ODU Digital Commons. For more information, please contact digitalcommons@odu.edu. Special Section Guest Editorial:Machine Learning in OpticsJonathan HoweTravis AxtellKhan IftekharuddinJonathan Howe, Travis Axtell, Khan Iftekharuddin, “Special Section Guest Editorial: MachineLearning in Optics,” Opt. Eng. 59(5), 051401 (2020), doi: 10.1117/1.OE.59.5.051401Downloaded From: https://www.spiedigitallibrary.org/journals/Optical-Engineering on 22 Jul 2020Terms of Use: https://www.spiedigitallibrary.org/terms-of-useOptical Engineering OpticalEngineering.SPIEDigitallibrary.org SPIE. Special Section Guest Editorial: Machine Learningin OpticsJonathan Howe,a Travis Axtell,b and Khan IftekharuddincaNVIDIA Corporation, Washington, DC, United StatesbBall Aerospace, Boulder, Colorado, United StatescOld Dominion University, Norfolk, Virginia, United StatesOne of the benefits of research using machine learning techniques is the plethora of opportunitiesfor creativity in problem solving. Certainly there is a lot of hype around artificial intelligence andthe anticipated impact on society. Nonetheless, machine learning is a rapidly evolving field dueto the large community of interdisciplinary researchers currently focused on the topic. Majorbreakthroughs in computer vision helped to inspire the intense focus on machine learning thatwe see across many fields of inquiry. This special section covers recent advances in machinelearning through innovation in the science of optical systems.Practicing researchers know the importance of data and good strategies for retention,analysis, and reproducible results. All data has variation, and the intent of machine learningtechniques is to capture that variation and produce algorithms that have meaningful results.Algorithm design is as much an art as a science to tailor for variation. In the papers presentedin this special section, the authors demonstrate novel approaches to solve some of the intricatechallenges in the optical sciences using machine learning tools.Zhang et al. present their novel approach of joint, sparse, model-based image fusion to fusetwo images from different imaging modes using a learned dictionary at multiple scales. Imagefusion is a current trending topic in machine learning research to bring forward the interest partsof each image into a single image. Human interpretation of the resulting scene is improved bycontext from each image.Kyono et al. perform image quality assessment for ground-based telescopes using a machinelearning approach. Their first application determines the resolvability prediction of an outputblind deconvolution given a collection of telescope images; the intent is to save supercomputerresources if the computational image will not be a good result. Their second application is pre-dicting the image quality on the Space-object National Imagery Interpretability Rating Scale(SNIIRS) as a means of estimating the information value of the resolved image.Optical systems design can be changed by our understanding of machine learning principlesto create novel, low-power implementations. Redman et al. use two different optical layouts toperform classification (binning of input data) by combining their optical design with compressedsensing theory using several low-resolution detectors. The approach casts the variation into asmaller decision space using optics. Redman notes that image quality does not need to bedesigned for human interpretability but rather can be matched to maximize performance of thealgorithm.Saeed et al. present an approach to perform handwritten script-to-text on scanned Arabicdocuments using maximally stable extremal regions as the feature vector. This is an interestingway to discern meaning in cursive handwriting across different writers, and we believe it hasapplicability to other problems in optics.Oliveira et al. use machine learning in shearography, an important application for precisionmanufacturing precision. The convolutional U-net algorithm is used to perform segmentation onthe interferogram to determine the locations of deformation. The algorithm shows promise onreduced measurement error with tighter bounds than conventional approaches. In the final paperin this special section, Theagarajan et al. combine a model-based approach with a convolutionalneural network to perform target recognition in inverse synthetic aperture RADAR (ISAR)imagery. The investigation demonstrates how the approaches complement one another under© 2020 Society of Photo-Optical Instrumentation Engineers (SPIE)Optical Engineering 051401-1 May 2020 • Vol. 59(5)Downloaded From: https://www.spiedigitallibrary.org/journals/Optical-Engineering on 22 Jul 2020Terms of Use: https://www.spiedigitallibrary.org/terms-of-usecommon degradations in image formation, such as from object motion and clutter. The discus-sion on performance for the different ISAR data representations is thought provoking.The guest editors wish to thank the Optical Engineering editorial team, the authors, andreviewers for their effort to present this section. Our goal with this special section is to inspirecontinued research into optics using machine learning where appropriate.Special Section Guest EditorialOptical Engineering 051401-2 May 2020 • Vol. 59(5)Downloaded From: https://www.spiedigitallibrary.org/journals/Optical-Engineering on 22 Jul 2020Terms of Use: https://www.spiedigitallibrary.org/terms-of-use

Special Section Guest Editorial: Machine Learning In Optics

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Special Section Guest Editorial: Machine Learning In Optics

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