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This special issue of Applied Optics contains selected papers reflecting the various disciplines that are needed for the design, implementation and advancement of imaging technology and systems, and it highlights the state-of-the-art research developments in the areas of modern imaging use.
© 2017 Optical Society of America
Modern imaging systems result from the interactions between various disciplines working in conjunction to enable their design and implementation for the intended application. The OSA Imaging and Applied Optics Congress brings together every year scientists and engineers from commercial, academic, and military fields, providing a comprehensive view of the latest developments in imaging and applied optical sciences and covering its forefront advances as well as diverse applications. This special issue of Applied Optics contains a subset of the work presented at OSA’s Imaging Congress in 2016 (Heidelberg, Germany) as well as contributions from the wider imaging and applied optics community. It highlights, in particular, work from four topical conferences of the annual congress: 1) Mathematics in Imaging; 2) Computational Optical Sensing and Imaging; 3) Imaging Systems and Applications; and 4) 3D Image Acquisition and Display: Technology, Perception and Applications.
Imaging technology and systems have numerous applications in industrial, military, consumer, and medical settings. The design of such modern imaging systems must factor the system as an integrated unit and optimize the performance for the specific application. There are numerous disciplines that are needed for the design and advancement of a modern imaging system. These disciplines include imaging optics, image sensors and optical detection, computational, adaptive, and compressive imaging, displays, and information extraction; all of which contribute to the optimization of the imaging system.
Assembling a complete imaging system thus requires the integration of optics, sensing, and dealing with the image resolution bounds, image processing algorithms, displays, and electronic hardware. The progress of imaging technology is typically driven by advances in these contributing fields, which spark new ideas to address fundamental questions, such as imaging aberrations, the phase retrieval problem, and the resolution limit of imaging modalities. This Modern Imaging feature provides a cross-section of the state-of-the-art of imaging technology, including 3D imaging, both at micro and macro scales. It is aimed at scientists, engineers, and practitioners interested in understanding how different materials, optical components, devices, systems, and image processing algorithms integrate to determine and influence imaging system capabilities and performance. This special feature’s intent is to bring together the components that define the imaging system and identify the research advances in the areas of use while emphasizing not only the scientific and the research related component of the field, but also the engineering state of the art relevant to high-tech industries and development engineers.
In the collection of papers presented within this feature issue, research on computational imaging for microscopy and super resolution [1–5], as well as computational imaging and modeling at macroscopic scales [6–8], digital holographic microscopy [9,10] and other holographic technology [11], image reconstruction [12,13], as well as motion analysis for ophthalmic systems [14], passive 3D imaging performed outdoors [15], hyperspectral imaging [16], white-light refectometry [17], optical coherence tomography [18–20], and flexible sensing integral imaging [21] can be found.
Acknowledgment
We thank all the authors for their fine contributions, the reviewers for their valuable comments and suggestions, and the Applied Optics Editors and Staff for their support and assistance. Special thanks to Dr. Joseph N. Mait, COSI co-chair at the OSA Imaging Congress 2016, who proposed this feature issue to OSA.
REFERENCES
1. G. Kim and R. Menon, “Numerical analysis of computational-cannula microscopy,” Appl. Opt. 56, D1–D7 (2017). [CrossRef]
2. N. Patwary, H. Shabani, A. Doblas, G. Saavedra, and C. Preza, “Experimental validation of a customized phase mask designed to enable efficient computational optical sectioning microscopy through wavefront encoding,” Appl. Opt. 56, D14–D23 (2017). [CrossRef]
3. C. Bermudez, F. Laguarta, C. Cadevall, A. Matilla, S. Ibañez, and R. Artigas, “Stent optical inspection system calibration and performance,” Appl. Opt. 56, D134–D141 (2017).
4. T. Ilovitsh, A. Ilovitsh, O. Wagner, and Z. Zalevsky, “Super resolved nanoscopy using Brownian motion of fluorescently labeled gold nanoparticles,” Appl. Opt. 56, 1365–1369 (2017).
5. T. Yaron, A. Klein, H. Duadi, and M. Fridman, “Temporal superresolution based on a localization microscopy algorithm,” Appl. Opt. 56, D24–D28 (2017). [CrossRef]
6. I. Sinharoy, P. Rangarajan, and M. P. Christensen, “Geometric model for an independently tilted lens and sensor with application for omnifocus imaging,” Appl. Opt. 56, D37–D46 (2017). [CrossRef]
7. P. Rangarajan, I. Sinharoy, P. Milojkovic, and M. P. Christensen, “Active computational imaging for circumventing resolution limits at macroscopic scales,” Appl. Opt. 56, D84–D107 (2017). [CrossRef]
8. C.-Y. Chi, S.-H. Qiu, G.-J. Lin, T.-J. Chen, Y.-J. Yang, and J.-J. Wu, “Effects of the vertically switching electric field on the photoelectric properties of polymer-stabilized blue-phase liquid crystal cells using the director model,” Appl. Opt. 56, D29–D36 (2017). [CrossRef]
9. M. Aakhte, V. Abbasian, E. A. Akhlaghi, A.-R. Moradi, A. Anand, and B. Javidi, “Microsphere-assisted super-resolved Mirau digital holographic microscopy for cell identification,” Appl. Opt. 56, D8–D13 (2017). [CrossRef]
10. S. Rawat, S. Komatsu, A. Markman, A. Anand, and B. Javidi, “Compact and field-portable 3D printed shearing digital holographic microscope for automated cell identification,” Appl. Opt. 56, D127–D133 (2017).
11. Y.-H. Seo, Y.-H. Lee, and D.-W. Kim, “ASIC chipset design to generate block-based complex holographic video,” Appl. Opt. 56, D52–D59 (2017). [CrossRef]
12. X. Zhang, B. Javidi, and M. K. Ng, “Automatic regularization parameter selection by generalized cross-validation for total variational Poisson noise removal,” Appl. Opt. 56, D47–D51 (2017). [CrossRef]
13. J. R. Alonso, “Fourier domain post-acquisition aperture reshaping from a multi-focus stack,” Appl. Opt. 56, D60–D65 (2017). [CrossRef]
14. S. Meimon, J. Jarosz, C. Petit, E. Gofas Salas, K. Grieve, J.-M. Conan, B. Emica, M. Paques, and K. Irsch, “Pupil motion analysis and tracking in ophthalmic systems equipped with wavefront sensing technology,” Appl. Opt. 56, D66–D71 (2017). [CrossRef]
15. S. Komatsu, A. Markman, A. Mahalanobis, K. Chen, and B. Javidi, “Three-dimensional integral imaging and object detection using long-wave infrared imaging,” Appl. Opt. 56, D120–D126 (2017). [CrossRef]
16. W. Wu, G.-Y. Chen, M.-Q. Wu, Z.-W. Yu, and K.-J. Chen, “Detection of diluted contaminants on chicken carcasses using a two-dimensional scatter plot based on a two-dimensional hyperspectral correlation spectrum,” Appl. Opt. 56, D72–D78 (2017). [CrossRef]
17. F. Hergert, “Spatially resolved thickness determination of rough thin films by reflectometry with polychromatic light,” Appl. Opt. 56, D79–D83 (2017). [CrossRef]
18. R. E. Wijesinghe, S.-Y. Lee, N. K. Ravichandran, S. Han, H. Jeong, Y. Han, H.-Y. Jung, P. Kim, M. Jeon, and J. Kim, “Optical coherence tomography-integrated, wearable (backpack-type), compact diagnostic imaging modality for in situ leaf quality assessment,” Appl. Opt. 56, D108–D114 (2017). [CrossRef]
19. K. Park, N. H. Cho, J. H. Jang, S. H. Lee, P. Kim, M. Jeon, S. A. Boppart, J. Kim, and W. Jung, “In vivo 3D imaging of the human tympanic membrane using a wide-field diagonal-scanning optical coherence tomography probe,” Appl. Opt. 56, D115–D119 (2017). [CrossRef]
20. A. Dubois, “Focus defect and dispersion mismatch in full-field optical coherence microscopy,” Appl. Opt. 56, D142–D150 (2017). [CrossRef]
21. X. Shen, A. Markman, and B. Javidi, “Three-dimensional profilometric reconstruction using flexible sensing integral imaging and occlusion removal,” Appl. Opt. 56, D151–D157 (2017). [CrossRef]