Influence of optimum balanced linear coma on disk spread functions

1978 ◽  
Vol 56 (1) ◽  
pp. 12-16
Author(s):  
A. K. Gupta ◽  
R. N. Singh ◽  
K. Singh
Keyword(s):  
Intensity Distribution ◽  
Point Spread Function ◽  
Optical Systems ◽  
Point Spread ◽  
Spread Function ◽  
Special Case ◽  
Optimum Balance

Disk spread functions are evaluated to study the performance of optical systems in the presence of linear coma. Optimum balance among various coma terms based on Strehl intensity criterion is used and the applicability of this balance to imaging of extended objects is examined. Graphical results of intensity distribution in the paraxial receiving plane for the diffraction images of extended circular targets for various sizes and azimuths are presented. Results for the point spread function in presence of optimum balanced linear coma come out as a special case and are also included.

Truncated apodizers for engineering the point spread function of optical systems

2021 ◽  
Author(s):  
Andra Naresh Kumar Reddy ◽  
Ramprasad Lachimala ◽  
Mahdieh Hashemi ◽  
Dasari Karuna Sagar
Keyword(s):  
Point Spread Function ◽  
Optical Systems ◽  
Point Spread ◽  
Spread Function

scholarly journals Randomly Multiplexed Diffractive Lens and Axicon for Spatial and Spectral Imaging

Micromachines ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 437 ◽  
Author(s):  
Vijayakumar Anand ◽  
Tomas Katkus ◽  
Saulius Juodkazis
Keyword(s):  
Intensity Distribution ◽  
Point Spread Function ◽  
Fresnel Zone ◽  
Spherical Wave ◽  
Optical Element ◽  
Intensity Pattern ◽  
Object Space ◽  
Point Spread ◽  
Spread Function ◽  
Complicated Object

A new hybrid diffractive optical element (HDOE) was designed by randomly multiplexing an axicon and a Fresnel zone lens. The HDOE generates two mutually coherent waves, namely a conical wave and a spherical wave, for every on-axis point object in the object space. The resulting self-interference intensity distribution is recorded as the point spread function. A library of point spread functions are recorded in terms of the different locations and wavelengths of the on-axis point objects in the object space. A complicated object illuminated by a spatially incoherent multi-wavelength source generated an intensity pattern that was the sum of the shifted and scaled point spread intensity distributions corresponding to every spatially incoherent point and wavelength in the complicated object. The four-dimensional image of the object was reconstructed using computer processing of the object intensity distribution and the point spread function library.

A NOVEL ANALYTICAL SPACE-VARIANT POINT SPREAD FUNCTION MODEL FOR UNDERCORRECTED OPTICAL SYSTEMS

2000 ◽  
Vol 10 (05n06) ◽  
pp. 305-313
Author(s):  
THOMAS P. COSTELLO ◽  
WASFY B. MIKHAEL
Keyword(s):  
Point Spread Function ◽  
Diffraction Theory ◽  
Optical Systems ◽  
Optical Transfer Function ◽  
Point Spread ◽  
Proposed Model ◽  
Direct Sampling ◽  
Spread Function ◽  
Optical Transfer ◽  
Space Variant

An analytical model is developed for the space-variant (SV) point-spread-function (PSF) of an undercorrected optical system with a rectangular aperture. The model accommodates broadening and shifting of the central lobe, as well as sidelobe asymmetry of the PSF, as field angle increases. These effects are exhibited by diffraction-based PSF models. The proposed model uses eight parameters for any specific field position, compared to ~ 210 parameters required for direct sampling of an individual PSF. The model is adapted to PSFs developed from diffraction theory using an adaptive system with gradient descent parameter adjustment. Consequently, the model is useful for applying certain SV digital image restoration methods because it significantly reduces the memory required to store PSF sample functions. In addition, the model does not require samples of the PSF or a DFT operation to obtain samples of the optical transfer function (OTF). Thus, the efficiency of SV restoration methods applied in the frequency domain, such as sectioning approaches, is further improved. Data presented confirms the accuracy and the computational advantage of the model by quantifying its adaptation to a physical PSF over a range of field angles.

scholarly journals Physics-based model of the adaptive-optics-corrected point spread function

2019 ◽  
Vol 628 ◽  
pp. A99 ◽  
Author(s):  
R. J. L. Fétick ◽  
T. Fusco ◽  
B. Neichel ◽  
L. M. Mugnier ◽  
O. Beltramo-Martin ◽  
...  
Keyword(s):  
Adaptive Optics ◽  
Point Spread Function ◽  
Simulated Data ◽  
Optical Systems ◽  
Field Mode ◽  
The Core ◽  
Point Spread ◽  
Power Spectral ◽  
Spread Function ◽  
Large Telescopes

Context. Adaptive optics (AO) systems greatly increase the resolution of large telescopes, but produce complex point spread function (PSF) shapes, varying in time and across the field of view. The PSF must be accurately known since it provides crucial information about optical systems for design, characterization, diagnostics, and image post-processing. Aims. We develop here a model of the AO long-exposure PSF, adapted to various seeing conditions and any AO system. This model is made to match accurately both the core of the PSF and its turbulent halo. Methods. The PSF model we develop is based on a parsimonious parameterization of the phase power spectral density, with only five parameters to describe circularly symmetric PSFs and seven parameters for asymmetrical ones. Moreover, one of the parameters is the Fried parameter r0 of the turbulence’s strength. This physical parameter is an asset in the PSF model since it can be correlated with external measurements of the r0, such as phase slopes from the AO real time computer (RTC) or site seeing monitoring. Results. We fit our model against end-to-end simulated PSFs using the OOMAO tool, and against on-sky PSFs from the SPHERE/ZIMPOL imager and the MUSE integral field spectrometer working in AO narrow-field mode. Our model matches the shape of the AO PSF both in the core and the halo, with a relative error smaller than 1% for simulated and experimental data. We also show that we retrieve the r0 parameter with sub-centimeter precision on simulated data. For ZIMPOL data, we show a correlation of 97% between our r0 estimation and the RTC estimation. Finally, MUSE allows us to test the spectral dependency of the fitted r0 parameter. It follows the theoretical λ6/5 evolution with a standard deviation of 0.3 cm. Evolution of other PSF parameters, such as residual phase variance or aliasing, is also discussed.

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