Estimation of non-uniform motion blur using a patch-based regression convolutional neural network.

The non-uniform blur of atmospheric turbulence can be modeled as a superposition of linear motion blur kernels at a patch level. We propose a regression convolutional neural network (CNN) to predict angle and length of a linear motion blur kernel for varying sized patches. We analyze the robustness of the network for different patch sizes and the performance of the network in regions where the characteristics of the blur are transitioning.

Estimation of modified Zernike coefficients from turbulence-degraded multispectral imagery using deep learning.

We investigate how wavelength diversity affects the performance of a deep-learning model that predicts the modified Zernike coefficients of turbulence-induced wavefront error from multispectral images. The ability to perform accurate predictions of the coefficients from images collected in turbulent conditions has potential applications in image restoration. The source images for this work were a point object and extended objects taken from a character-based dataset, and a wavelength-dependent simulation was developed that applies the effects of isoplanatic atmospheric turbulence to the images.

The Development and Demonstration of the Portable Acousto-Optic Spectrometer for Astrobiology in Cave Environments.

Planetary caves are desirable environments for the search for biosignatures corresponding to extant or extinct extraterrestrial life due to the protection they offer from surface-level solar radiation and ionizing particles. Near-infrared (NIR) reflectance spectroscopy is one of a multitude of techniques that, when taken together, can provide a comprehensive understanding of the geomicrobiology in planetary subsurface regions. To that end, we developed two portable NIR spectrometers that employ acousto-optic tunable filters and demonstrated them in three geochemically distinct cave environments.

Low-wavenumber compensation with Zernike tilt for non-Kolmogorov turbulence phase screens.

The fast-Fourier-transform-based filtering method for phase screen generation remains popular for numerical simulation of optical propagation through turbulence; however, these screens inherently underrepresent the spectral density at low wavenumbers. Here, the "Z-tilt" approach is explored to augment the spectral density at low wavenumbers by adding a random phase tilt, which is derived from the wavefront phase statistics of a Zernike polynomial basis. This approach is computationally efficient and can be applied to any statistically homogeneous and isotropic refractive index field.