Phase retrieval on broadband and under-sampled images for the JWST testbed telescope

Smith, Jeffrey S.; Aronstein, David L.; Dean, Bruce H.; Acton, D. Scott

doi:10.1117/12.825636

Cited by 14 publications

(7 citation statements)

References 12 publications

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“…The most common type of measurement diversity is focus diversity, as will be used for the James Webb Space Telescope [43,47]. Other forms of diversity include wavelength diversity, piston diversity (for segmented systems), actuator poking diversity, and transverse translation diversity (of a structure or an illumination beam relative to the object) [48,49] and diversity of field position to assess misalignments [50]. Diverse measurements are important under stressing conditions, such as when dealing with broadband or undersampled data [50,51] or when reconstructing amplitude as well as phase [46].…”

Section: Wavefront Sensing For Opticsmentioning

confidence: 99%

“…Other forms of diversity include wavelength diversity, piston diversity (for segmented systems), actuator poking diversity, and transverse translation diversity (of a structure or an illumination beam relative to the object) [48,49] and diversity of field position to assess misalignments [50]. Diverse measurements are important under stressing conditions, such as when dealing with broadband or undersampled data [50,51] or when reconstructing amplitude as well as phase [46]. When one measures two planes of intensity that are very near to one another, such that one can estimate the partial derivative of the 3D intensity with respect to the axial coordinate, then one can also use what is commonly called the transport-of-intensity approach [52].…”

Section: Wavefront Sensing For Opticsmentioning

confidence: 99%

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Phase retrieval algorithms: a personal tour [Invited]

2012

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Section: Wavefront Sensing For Opticsmentioning

confidence: 99%

Section: Wavefront Sensing For Opticsmentioning

confidence: 99%

Phase retrieval algorithms: a personal tour [Invited]

2012

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“…For the alignment of HST and JWST telescopes, the information lost from under-sampled images has to be reconstructed using dithering or drizzle method. Then the phase retrieval and phase diversity methods are used to estimate the wavefront errors from the reconstructed images [20]- [23]. The dithering method is one of the most popular image reconstruction methods based on several under-sampled images with low resolutions achieved by subpixel shifts of the detector to recover a high resolution image, which aims at solving the under-sampled problem of the HST telescope.…”

Section: Introductionmentioning

confidence: 99%

Telescopes Alignment Using the Sharpness Function Method Based on Undersampled Images

Liu

Zhang

et al. 2019

IEEE Photonics J.

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In astronomy, the images are sometimes undersampled. In the previous research works, image reconstructions have to be employed to recover the lost information from a set of undersampled images to achieve a high-resolution image using dithering or drizzle methods, which increase the complexity of alignment processes and make the real-time correction impossible. In this paper, the telescope is aligned by changing the positions of the secondary mirror using sharpness function method combined with the stochastic parallel gradient descent algorithm based on both well-sampled and undersampled images without image reconstructions. To improve the accuracy and robustness of the alignment, a new metric called relative root mean square error is proposed. Both numerical simulations and experiments are implemented, and the alignment precisions are measured by wavefront residual errors using Shack-Hartmann wavefront sensors. The results show that the correction processes can converge stably and quickly whether the images are well-sampled or undersampled. In experiments, the average wavefront error is 0.0595 λ for undersampled images and 0.0548 λ for well-sampled images after the telescope alignment, indicating that the misalignments are well compensated for both well-sampled and undersampled images.

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“…Generally speaking, image-based techniques perform best when the detected point-spread function (PSF) intensity data is critically sampled (Q = 2, where Q = λ F # ( ) pixel size ( ) ). Recent work has been done to extend the range of image-based techniques to undersampled data [3,4]. We used one of these undersampled techniques, a nonlinear-optimization based, PDPR algorithm [3], to sense the low-order wavefront error of a thermal infrared system across the field-of-view.…”

Section: Introductionmentioning

confidence: 99%