Hybrid phase-diverse phase retrieval algorithm in wavefront sensing for segmented optics

Heng Mao; Xiao Wang; Dazun Zhao

doi:10.1117/12.791169

12 March 2008 Hybrid phase-diverse phase retrieval algorithm in wavefront sensing for segmented optics

Heng Mao, Xiao Wang, Dazun Zhao

Proceedings Volume 6624, International Symposium on Photoelectronic Detection and Imaging 2007: Optoelectronic System Design, Manufacturing, and Testing; 66241M (2008) https://doi.org/10.1117/12.791169
Event: International Symposium on Photoelectronic Detection and Imaging: Technology and Applications 2007, 2007, Beijing, China

Abstract

Wavefront sensing of the segmented optics usually includes two steps. The first step is to sense and correct each segment's position error (co-phasing error) and figure error (aberration), and the second step is to sense the residual wavefront error over the whole aperture. However, due to the limited correction capability and accuracy of the figure sensing and co-phasing sensing in the first step, the original fine wavefront sensing (WFS) algorithm in the second step, the Modified Baseline algorithm, would be failed once the absolute figure difference at nearby edges of nearby segments exceeds λ/2. Thus a Hybrid Phase-diverse Phase Retrieval (HPDPR) algorithm, which adds the Levenburg-Marquardt (LM) algorithm to eliminate each segment's residual position error for decreasing the absolute figure difference, is proposed. Simulations have proven the validity of the HPDPR algorithm, and shown its robustness and high WFS accuracy.

Citation Download Citation

Heng Mao, Xiao Wang, and Dazun Zhao "Hybrid phase-diverse phase retrieval algorithm in wavefront sensing for segmented optics", Proc. SPIE 6624, International Symposium on Photoelectronic Detection and Imaging 2007: Optoelectronic System Design, Manufacturing, and Testing, 66241M (12 March 2008); https://doi.org/10.1117/12.791169

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KEYWORDS

Wavefront sensors

Image segmentation

Phase retrieval

Computer simulations

Image processing algorithms and systems

Mirrors

Data modeling

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