Thin holographic camera with integrated reference distribution.

Off-axis digital holography typically uses a beam splitter to combine reference and object waves at an angle matched to the sampling period of the sensor array. The beam splitter determines the thickness of the recording system. This paper describes and demonstrates a total internal reflection hologram that replaces the beam splitter and enables hologram recording over a large aperture with a thin camera.

Image-based registration for synthetic aperture holography.

High pixel count apertures for digital holography may be synthesized by scanning smaller aperture detector arrays. Characterization and compensation for registration errors in the detector array position and pitch and for phase instability between the reference and object field is a major challenge in scanned systems. We use a secondary sensor to monitor phase and image-based registration parameter estimators to demonstrate near diffraction-limited resolution from a 63.

Video-rate compressive holographic microscopic tomography.

Compressive holography enables 3D reconstruction from a single 2D holographic snapshot for objects that can be sparsely represented in some basis. The snapshot mode enables tomographic imaging of microscopic moving objects. We demonstrate video-rate tomographic image acquisition of two live water cyclopses with 5.

Multiframe image estimation for coded aperture snapshot spectral imagers.

A coded aperture snapshot spectral imager (CASSI) estimates the three-dimensional spatiospectral data cube from a snapshot two-dimensional coded projection, assuming that the scene is spatially and spectrally sparse. For less spectrally sparse scenes, we show that the use of multiple nondegenerate snapshots can make data cube recovery less ill-posed, yielding improved spatial and spectral reconstruction fidelity. Additionally, data acquisition can be easily scaled to meet the time/resolution requirements of the scene with little modification or extension of the original CASSI hardware.

Compressive holography of diffuse objects.

We propose an estimation-theoretic approach to the inference of an incoherent 3D scattering density from 2D scattered speckle field measurements. The object density is derived from the covariance of the speckle field. The inference is performed by a constrained optimization technique inspired by compressive sensing theory.

Generalized sampling using a compound-eye imaging system for multi-dimensional object acquisition.

In this paper, we propose generalized sampling approaches for measuring a multi-dimensional object using a compact compound-eye imaging system called thin observation module by bound optics (TOMBO). This paper shows the proposed system model, physical examples, and simulations to verify TOMBO imaging using generalized sampling. In the system, an object is modulated and multiplied by a weight distribution with physical coding, and the coded optical signal is integrated on to a detector array.

Inline holographic coherent anti-Stokes Raman microscopy.

We demonstrate a simple approach for inline holographic coherent anti-Stokes Raman scattering (CARS) microscopy, in which a layer of uniform nonlinear medium is placed in front of a specimen to be imaged. The reference wave created by four-wave mixing in the nonlinear medium can interfere with the CARS signal generated in the specimen to result in an inline hologram. We experimentally and theoretically investigate the inline CARS holography and show that it has chemical selectivity and can allow for three-dimensional imaging.

Identification of fluorescent beads using a coded aperture snapshot spectral imager.

We apply a coded aperture snapshot spectral imager (CASSI) to fluorescence microscopy. CASSI records a two-dimensional (2D) spectrally filtered projection of a three-dimensional (3D) spectral data cube. We minimize a convex quadratic function with total variation (TV) constraints for data cube estimation from the 2D snapshot.

Imaging through turbulence using compressive coherence sensing.

Previous studies have shown that the isoplanatic distortion due to turbulence and the image of a remote object may be jointly estimated from the 4D mutual intensity across an aperture. This Letter shows that decompressive inference on a 2D slice of the 4D mutual intensity, as measured by a rotational shear interferometer, is sufficient for estimation of sparse objects imaged through turbulence. The 2D slice is processed using an iterative algorithm that alternates between estimating the sparse objects and estimating the turbulence-induced phase screen.

Compressive holography.

Compressive sampling enables signal reconstruction using less than one measurement per reconstructed signal value. Compressive measurement is particularly useful in generating multidimensional images from lower dimensional data. We demonstrate single frame 3D tomography from 2D holographic data.

Signal-processing approaches for image-resolution restoration for TOMBO imagery.

Thin observation module by bounded optics (TOMBO) is an optical system that achieves compactness and thinness by replacing a conventional large full aperture by a lenslet array with several smaller apertures. This array allows us to collect diverse low-resolution measurements. Finding an efficient way of combining these diverse measurements to make a high-resolution image is an important research problem.

Phase retrieval from noisy data based on minimization of penalized I-divergence.

We study noise artifacts in phase retrieval based on minimization of an information-theoretic discrepancy measure called Csiszár's I-divergence. We specifically focus on adding Poisson noise to either the autocorrelation of the true image (as in astronomical imaging through turbulence) or the squared Fourier magnitudes of the true image (as in x-ray crystallography). Noise effects are quantified via various error metrics as signal-to-noise ratios vary.

Convergence of the Schulz-Snyder phase retrieval algorithm to local minima.

The Schulz-Snyder iterative algorithm for phase retrieval attempts to recover a nonnegative function from its autocorrelation by minimizing the I-divergence between a measured autocorrelation and the autocorrelation of the estimated image. We illustrate that the Schulz-Snyder algorithm can become trapped in a local minimum of the I-divergence surface. To show that the estimates found are indeed local minima, sufficient conditions involving the gradient and the Hessian matrix of the I-divergence are given.