Least-squares Fourier phase estimation from the modulo 2Pi bispectrum phase

The recovery of Fourier phases from measurements of the bispectrum occupies a vital role in many astronomical speckle imaging schemes. In arecent paper [J. Opt. Soc. Am. A 7, 14 (1990)] it was suggested that a least-squares solution to this problem must fail if the bispectrum phase is known only modulo 2π. Here an alternative nonlinear least-squares algorithm is presented that differs from the linear method discussed in the aforementioned paper and that permits the fitting of Fourier phases directly to modulo 2π measurements of the bispectrum phase, thus eliminating any need for phase unwrapping. Numerical simulations of this method confirm that it is reliable and robust in the presence of noise and verify its enhanced performance when compared with a linear least-squares method that includes the unwrapping of the bispectral phase before Fourier phase retrieval

QuaSI: Quantile Sparse Image Prior for Spatio-Temporal Denoising of Retinal OCT Data

Optical coherence tomography (OCT) enables high-resolution and non-invasive 3D imaging of the human retina but is inherently impaired by speckle noise. This paper introduces a spatio-temporal denoising algorithm for OCT data on a B-scan level using a novel quantile sparse image (QuaSI) prior. To remove speckle noise while preserving image structures of diagnostic relevance, we implement our QuaSI prior via median filter regularization coupled with a Huber data fidelity model in a variational approach. For efficient energy minimization, we develop an alternating direction method of multipliers (ADMM) scheme using a linearization of median filtering. Our spatio-temporal method can handle both, denoising of single B-scans and temporally consecutive B-scans, to gain volumetric OCT data with enhanced signal-to-noise ratio. Our algorithm based on 4 B-scans only achieved comparable performance to averaging 13 B-scans and outperformed other current denoising methods.Comment: submitted to MICCAI'1

Simulating the WFIRST coronagraph Integral Field Spectrograph

A primary goal of direct imaging techniques is to spectrally characterize the atmospheres of planets around other stars at extremely high contrast levels. To achieve this goal, coronagraphic instruments have favored integral field spectrographs (IFS) as the science cameras to disperse the entire search area at once and obtain spectra at each location, since the planet position is not known a priori. These spectrographs are useful against confusion from speckles and background objects, and can also help in the speckle subtraction and wavefront control stages of the coronagraphic observation. We present a software package, the Coronagraph and Rapid Imaging Spectrograph in Python (crispy) to simulate the IFS of the WFIRST Coronagraph Instrument (CGI). The software propagates input science cubes using spatially and spectrally resolved coronagraphic focal plane cubes, transforms them into IFS detector maps and ultimately reconstructs the spatio-spectral input scene as a 3D datacube. Simulated IFS cubes can be used to test data extraction techniques, refine sensitivity analyses and carry out design trade studies of the flight CGI-IFS instrument. crispy is a publicly available Python package and can be adapted to other IFS designs.Comment: 15 page

PeX 1. Multi-spectral expansion of residual speckles for planet detection

The detection of exoplanets in coronographic images is severely limited by residual starlight speckles. Dedicated post-processing can drastically reduce this "stellar leakage" and thereby increase the faintness of detectable exoplanets. Based on a multi-spectral series expansion of the diffraction pattern, we derive a multi-mode model of the residuals which can be exploited to estimate and thus remove the residual speckles in multi-spectral coronographic images. Compared to other multi-spectral processing methods, our model is physically grounded and is suitable for use in an (optimal) inverse approach. We demonstrate the ability of our model to correctly estimate the speckles in simulated data and demonstrate that very high contrasts can be achieved. We further apply our method to removing speckles from a real data cube obtained with the SPHERE IFS instrument.Comment: accepted for publication in MNRAS on 25th of August 2017, 17 pages, 15 figure