Any beam that propagates through optical turbulence will experience distortions in both its amplitude and phase, leading to various effects such as beam wandering, beam spreading, and irradiance fluctuations. Reconstructing the complete field of a perturbed beam is a challenging task due to the dynamic nature of these effects. Interferometric wavefront reconstruction techniques—such as those based on holography—are commonly used but are hindered by their sensitivity to environmental disturbances and alignment errors. However, new complex phase retrieval methods based on propagation equations have emerged, which do not require prior knowledge of the beam to be reconstructed and are suitable for amplitude or phase objects, or both. We propose an experimental implementation of a complex phase retrieval technique for characterizing Gaussian beams propagating through optical turbulence, using binary amplitude modulation with a digital micro-mirror device (DMD). This approach is ideal for dynamic applications and has enabled us to achieve experimental high-speed complex wavefront reconstruction of optical beams through controlled real turbulence. This experiment corresponds to the initial step in our research focused on gaining a deeper understanding of optical turbulence from an experimental perspective.
We present the design and implementation of an adaptive optics test bench recently built at the School of Electrical Engineering of the Pontificia Universidad Católica de Valparaíso in Chile. The flexible design of the PULPOS bench incorporates state-of-the-art, high-speed spatial light modulators for atmospheric turbulence emulation and wavefront correction, a deformable mirror for modulation, and a variety of wavefront sensors such as a pyramid wavefront sensor. PULPOS serves as a platform for research on adaptive optics and wavefront reconstruction using artificial intelligence techniques, as well as for educational purposes.
In this work, we evaluate a especially crafted deep convolutional neural network to provide with estimations of the wavefront aberration modes directly from pyramidal wavefront sensor (PyWFS) images. Overall, the use of deep neural networks allow to improve the estimation performance as well as the operational range of the PyWFS, especially when considering cases of strong turbulence or bad seeing ratios D0/r0. Our preliminary results provide with evidence that by using neural nets, instead of the classic linear estimation methods, we can obtain a low modulation sensitivity response while extending the linearity range of the PyWFS, reducing the residual variance by a factor of 1.6 when dealing with a r0 as low as a few centimeters.
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