"Off-axis sparse aperture imaging using phase optimization techniques for application in wide-area imaging systems" by Abhijit Mahalanobis

Seminar by Abhijit Mahalanobis, Professor at the Center for Research in Computer Vision, University of Central Florida, Orlando

Abstract

Sparse apertures find imaging applications in diverse fields such as astronomy and medicine. We are motivated by the design of a wide-area imaging system where sparse apertures can be used to construct novel and efficient optical designs. Specifically, we investigate the use of sparse apertures for off-axis imaging at infrared wavelengths while combating the effects of chromaticity to preserve resolution.
In principle, several such sparse apertures can be interleaved within a common aperture to simultaneously image in multiple directions. This can ultimately lead to the design of wide-area imaging systems that require considerably less optical and electronic hardware. The resolution achievable using a sparse aperture is the same as that of a fully open aperture. In the case of off-axis imaging, however, the point spread function (PSF) introduces a blur due to chromaticity that degrades the resolution of the system. Of course, the blur can be eliminated by imaging at a single wavelength. However the signal-to-noise ratio (SNR) is poor, which ultimately degrades image quality. To improve SNR, it is necessary to widen the band of wavelengths, which of course degrades resolution due to chromaticity. Hence there is a fundamental trade between the SNR and the resolution as a function of bandwidth. We show that by using a combination of microprisms and phase optimized micropistons it is possible to reduce the chromatic blur over a band of wavelengths and improve the PSF considerably to restore the resolution of the image. The concepts are validated by means of simulations and verified with experimental data to demonstrate the advantages of phase optimized micropistons in off-axis sparse aperture imaging systems.

This seminar will be held in english.