Nonlinear optical systems for image processing and complex field monitoring

1999 ◽  
Author(s):  
Anna D. Kudryavtseva ◽  
Albina I. Sokolovskaya ◽  
Nicolaii V. Tcherniega
Keyword(s):  
Image Processing ◽  
Nonlinear Optical ◽  
Field Monitoring ◽  
Optical Systems ◽  
Complex Field

Squeezing spectra for nonlinear optical systems

1985 ◽  
Vol 32 (5) ◽  
pp. 2887-2892 ◽  
Author(s):  
M. J. Collett ◽  
D. F. Walls
Keyword(s):  
Nonlinear Optical ◽  
Optical Systems ◽  
Squeezing Spectra

A proposal of spin-and charge-modulated open-shell nonlinear optical systems

2005 ◽  
Vol 154 (1-3) ◽  
pp. 309-312 ◽  
Author(s):  
M. Nakano ◽  
B. Champagne ◽  
E. Botek ◽  
R. Kishi ◽  
T. Nitta ◽  
...  
Keyword(s):  
Nonlinear Optical ◽  
Open Shell ◽  
Optical Systems

Configurational disorder and the local field effects in nonlinear optical systems

1997 ◽  
Vol 1 (6) ◽  
pp. 169 ◽  
Author(s):  
B. Vugmeister ◽  
Alexei Bulatov ◽  
Herschel Rabitz
Keyword(s):  
Local Field ◽  
Nonlinear Optical ◽  
Optical Systems ◽  
Field Effects

Fourier Analysis and Nonlinear Optical Systems

1969 ◽  
Vol 8 (3) ◽  
pp. 563 ◽  
Author(s):  
Adolph Baker ◽  
David Korff
Keyword(s):  
Fourier Analysis ◽  
Nonlinear Optical ◽  
Optical Systems

Nonlinear Optical Image Processing with Bacteriorhodopsin Polymer Films

2006 ◽  
Vol 446 (1) ◽  
pp. 273-294 ◽  
Author(s):  
Chandra S. Yelleswarapu ◽  
Pengfei Wu ◽  
Sri-Rajasekhar Kothapalli ◽  
D. V. G. L. N. Rao
Keyword(s):  
Image Processing ◽  
Polymer Films ◽  
Nonlinear Optical ◽  
Optical Image ◽  
Optical Image Processing

Differentiable Compound Optics and Processing Pipeline Optimization for End-to-end Camera Design

2021 ◽  
Vol 40 (2) ◽  
pp. 1-19
Author(s):  
Ethan Tseng ◽  
Ali Mosleh ◽  
Fahim Mannan ◽  
Karl St-Arnaud ◽  
Avinash Sharma ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Optical Design ◽  
Neural Nets ◽  
Fine Tuning ◽  
Optical Systems ◽  
Design Tools ◽  
End To End ◽  
Application Specific ◽  
Network Processing

Most modern commodity imaging systems we use directly for photography—or indirectly rely on for downstream applications—employ optical systems of multiple lenses that must balance deviations from perfect optics, manufacturing constraints, tolerances, cost, and footprint. Although optical designs often have complex interactions with downstream image processing or analysis tasks, today’s compound optics are designed in isolation from these interactions. Existing optical design tools aim to minimize optical aberrations, such as deviations from Gauss’ linear model of optics, instead of application-specific losses, precluding joint optimization with hardware image signal processing (ISP) and highly parameterized neural network processing. In this article, we propose an optimization method for compound optics that lifts these limitations. We optimize entire lens systems jointly with hardware and software image processing pipelines, downstream neural network processing, and application-specific end-to-end losses. To this end, we propose a learned, differentiable forward model for compound optics and an alternating proximal optimization method that handles function compositions with highly varying parameter dimensions for optics, hardware ISP, and neural nets. Our method integrates seamlessly atop existing optical design tools, such as Zemax . We can thus assess our method across many camera system designs and end-to-end applications. We validate our approach in an automotive camera optics setting—together with hardware ISP post processing and detection—outperforming classical optics designs for automotive object detection and traffic light state detection. For human viewing tasks, we optimize optics and processing pipelines for dynamic outdoor scenarios and dynamic low-light imaging. We outperform existing compartmentalized design or fine-tuning methods qualitatively and quantitatively, across all domain-specific applications tested.

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