Cross-talk compensation of a spatial light modulator for iterative phase retrieval applications

Calibration of the pixel crosstalk in spatial light modulators for 4f pulse shaping

The targeted shaping of femtosecond pulses in 4f pulse shapers is complicated by, among other factors, the crosstalk between adjacent pixels of a spatial light modulator (SLM). Current methods for the crosstalk evaluation require setting up a different experiment, which is highly inconvenient. Here, we propose a simple procedure to extract the pixel crosstalk within the standard SLM calibration used in pulse shaping. The calibration is based on an analysis of the contrast of a periodic modulation in the spectra induced via SLM. We demonstrate the calibration procedure on a liquid-crystal-based SLM and show that we attain a constant crosstalk effect represented by a Gaussian function with σ=1.0 pix over a broad operational range of the SLM.

Correcting the Proximity Effect in Nanophotonic Phased Arrays

Thermally modulated Nanophotonic Phased Arrays (NPAs) can be used as phase-only holographic displays. Compared to the holographic displays based on Liquid Crystal on Silicon Spatial Light Modulators (LCoS SLMs), NPAs have the advantage of integrated light source and high refresh rate. However, the formation of the desired wavefront requires accurate modulation of the phase which is distorted by the thermal proximity effect. This problem has been largely overlooked and existing approaches to similar problems are either slow or do not provide a good result in the setting of NPAs. We propose two new algorithms based on the iterative phase retrieval algorithm and the proximal algorithm to address this challenge. We have carried out computational simulations to compare and contrast various algorithms in terms of image quality and computational efficiency. This work is going to benefit the research on NPAs and enable the use of large-scale NPAs as holographic displays.

Phase extraction neural network (PhENN) with coherent modulation imaging (CMI) for phase retrieval at low photon counts

Imaging with low-dose light is of importance in various fields, especially when minimizing radiation-induced damage onto samples is desirable. The raw image captured at the detector plane is then predominantly a Poisson random process with Gaussian noise added due to the quantum nature of photo-electric conversion. Under such noisy conditions, highly ill-posed problems such as phase retrieval from raw intensity measurements become prone to strong artifacts in the reconstructions; a situation that deep neural networks (DNNs) have already been shown to be useful at improving. Here, we demonstrate that random phase modulation on the optical field, also known as coherent modulation imaging (CMI), in conjunction with the phase extraction neural network (PhENN) and a Gerchberg-Saxton-Fienup (GSF) approximant, further improves resilience to noise of the phase-from-intensity imaging problem. We offer design guidelines for implementing the CMI hardware with the proposed computational reconstruction scheme and quantify reconstruction improvement as function of photon count.

Mode Selective Up-conversion Detection with Turbulence

We experimentally study a nonlinear optical approach to selective manipulation and detection of structured images mixed with turbulent noise. Unlike any existing adaptive-optics method by applying compensating modulation directly on the images, here we account for the turbulence indirectly, by modulating only the pump driving the nonlinear process but not the images themselves. This indirect approach eliminates any signal modulation loss or noise, while allowing more flexible and capable operations. Using specifically sum frequency generation in a lithium niobate crystal, we demonstrate selective upconversion of Laguerre-Gaussian spatial modes mixed with turbulent noise. The extinction reaches ~40 dB without turbulence, and maintains ~20 dB in the presence of strong turbulence. This technique could find utilities in classical and quantum communications, compressive imaging, pattern recognition, and so on.

WISH: wavefront imaging sensor with high resolution

Wavefront sensing is the simultaneous measurement of the amplitude and phase of an incoming optical field. Traditional wavefront sensors such as Shack-Hartmann wavefront sensor (SHWFS) suffer from a fundamental tradeoff between spatial resolution and phase estimation and consequently can only achieve a resolution of a few thousand pixels. To break this tradeoff, we present a novel computational-imaging-based technique, namely, the Wavefront Imaging Sensor with High resolution (WISH). We replace the microlens array in SHWFS with a spatial light modulator (SLM) and use a computational phase-retrieval algorithm to recover the incident wavefront. This wavefront sensor can measure highly varying optical fields at more than 10-megapixel resolution with the fine phase estimation. To the best of our knowledge, this resolution is an order of magnitude higher than the current noninterferometric wavefront sensors. To demonstrate the capability of WISH, we present three applications, which cover a wide range of spatial scales. First, we produce the diffraction-limited reconstruction for long-distance imaging by combining WISH with a large-aperture, low-quality Fresnel lens. Second, we show the recovery of high-resolution images of objects that are obscured by scattering. Third, we show that WISH can be used as a microscope without an objective lens. Our study suggests that the designing principle of WISH, which combines optical modulators and computational algorithms to sense high-resolution optical fields, enables improved capabilities in many existing applications while revealing entirely new, hitherto unexplored application areas.

Determination of the complex refractive index segments of turbid sample with multispectral spatially modulated structured light and models approximation

Spectral data enabling the derivation of a biological tissue sample's complex refractive index (CRI) can provide a range of valuable information in the clinical and research contexts. Specifically, changes in the CRI reflect alterations in tissue morphology and chemical composition, enabling its use as an optical marker during diagnosis and treatment. In the present work, we report a method for estimating the real and imaginary parts of the CRI of a biological sample using Kramers-Kronig (KK) relations in the spatial frequency domain. In this method, phase-shifted sinusoidal patterns at single high spatial frequency are serially projected onto the sample surface at different near-infrared wavelengths while a camera mounted normal to the sample surface acquires the reflected diffuse light. In the offline analysis pipeline, recorded images at each wavelength are converted to spatial phase maps using KK analysis and are then calibrated against phase-models derived from diffusion approximation. The amplitude of the reflected light, together with phase data, is then introduced into Fresnel equations to resolve both real and imaginary segments of the CRI at each wavelength. The technique was validated in tissue-mimicking phantoms with known optical parameters and in mouse models of ischemic injury and heat stress. Experimental data obtained indicate variations in the CRI among brain tissue suffering from injury. CRI fluctuations correlated with alterations in the scattering and absorption coefficients of the injured tissue are demonstrated. This technique for deriving dynamic changes in the CRI of tissue may be further developed as a clinical diagnostic tool and for biomedical research applications. To the best of our knowledge, this is the first report of the estimation of the spectral CRI of a mouse head following injury obtained in the spatial frequency domain.

Backplane aberration calibration of spatial light modulators using a phase-retrieval algorithm

The calibration and correction of backplane aberration of a liquid-crystal spatial light modulator (LCSLM) are important for its proper functioning. To simplify the calibration procedurally, we study a random-illumination-phase-retrieval-based method. Our method improves the convergence of the phase-retrieval-based calibration and reduces the calibration complexity. However, the cross talk of the LCSLM deteriorates the calibration performance. We determined the relationship between the probability density function of the random phases and the light-intensity pattern and proposed an algorithm to compensate for the cross talk. We conducted a series of simulations to test the performance of the above-mentioned algorithms. The results show that our algorithms are effective and outperform other methods.