Tengfei Wu

Single-shot diffraction-limited imaging through scattering layers via bispectrum analysis

Recently introduced speckle correlations-based techniques enable noninvasive imaging of objects hidden behind scattering layers. In these techniques, the hidden object Fourier amplitude is retrieved from the scattered light autocorrelation, and the lost Fourier phase is recovered via iterative phase-retrieval algorithms, which suffer from convergence to wrong local minimums solutions and cannot solve ambiguities in object orientation. Here, inspired by notions used in astronomy, we experimentally demonstrate that in addition to Fourier amplitude, the object-phase information is naturally and inherently encoded in the scattered light bispectrum (the Fourier transform of triple correlation) and can also be extracted from a single high-resolution speckle pattern, based on which we present a single-shot imaging scheme to deterministically and unambiguously retrieve diffraction-limited images of objects hidden behind scattering layers.

Simulation and experimental verification for imaging of gray-scale objects through scattering layers

We analyze the imaging of gray-scale objects through highly scattering layers. The theoretical investigation with numerical simulations shows that the contrast of the speckle autocorrelations varies regularly with the change of the gray scale of the object. Therefore, gray information is well contained in the autocorrelations of the speckle patterns, and gray-scale objects are able to be exacted from these autocorrelations via speckle correlation technology. Combined with phase retrieval via the generalized approximate message passing algorithm, recovery of the objects is realized and accurate gray-scale reconstruction is demonstrated via numerical simulations. Experiment results further demonstrate the good performance of the scheme in the imaging of gray-scale objects through scattering layers. Particularly, this work will be beneficial for applications of imaging through turbid media in biomedical and biophotonics imaging.

Imaging through a thin scattering layer and jointly retrieving the point-spread-function using phase-diversity

Recently introduced angular-memory-effect based techniques enable non-invasive imaging of objects hidden behind thin scattering layers. However, both the speckle-correlation and the bispectrum analysis are based on the statistical average of large amounts of speckle grains, which determines that they can hardly access the important information of the point-spread-function (PSF) of a highly scattering imaging system. Here, inspired by notions used in astronomy, we present a phase-diversity speckle imaging scheme, based on recording a sequence of intensity speckle patterns at various imaging planes, and experimentally demonstrate that in addition to being able to retrieve the image of hidden objects, we can also simultaneously estimate the pupil function and the PSF of a highly scattering imaging system without any guide-star nor reference.

High-efficiency imaging through scattering media in noisy environments via sparse image reconstruction

Abstract. High-efficiency imaging through highly scattering media is urgently desired for various applications. Imaging speed and imaging quality, which determine the imaging efficiency, are two inevitable indices for any optical imaging area. Based on random walk analysis in statistical optics, the elements in a transmission matrix (TM) actually obey Gaussian distribution. Instead of dealing with large amounts of data contained in TM and speckle pattern, imaging can be achieved with only a small number of the data via sparse representation. We make a detailed mathematical analysis of the elements-distribution of the TM of a scattering imaging system and study the imaging method of sparse image reconstruction (SIR). More specifically, we focus on analyzing the optimum sampling rates for the imaging of different structures of targets, which significantly influences both imaging speed and imaging quality. Results show that the optimum sampling rate exists in any noise-level environment if a target can be sparsely represented, and by searching for the optimum sampling rate, it can effectively balance the imaging quality and the imaging speed, which can maximize the imaging efficiency. This work is helpful for practical applications of imaging through highly scattering media with the SIR method.

Total variation optimization for imaging through turbid media with transmission matrix

Abstract. With the transmission matrix (TM) of the whole optical system measured, the image of the object behind a turbid medium can be recovered from its speckle field by means of an image reconstruction algorithm. Instead of Tikhonov regularization algorithm (TRA), the total variation minimization by augmented Lagrangian and alternating direction algorithms (TVAL3) is introduced to recover object images. As a total variation (TV)-based approach, TVAL3 allows to effectively damp more noise and preserve more edges compared with TRA, thus providing more outstanding image quality. Different levels of detector noise and TM-measurement noise are successively added to analyze the antinoise performance of these two algorithms. Simulation results show that TVAL3 is able to recover more details and suppress more noise than TRA under different noise levels, thus providing much more excellent image quality. Furthermore, whether it be detector noise or TM-measurement noise, the reconstruction images obtained by TVAL3 at SNR=15u2009u2009dB are far superior to those by TRA at SNR=50u2009u2009dB.

Focusing of light through turbid media by curve fitting optimization

Abstract. The construction of wavefront phase plays a critical role in focusing light through turbid media. We introduce the curve fitting algorithm (CFA) into the feedback control procedure for wavefront optimization. Unlike the existing continuous sequential algorithm (CSA), the CFA locates the optimal phase by fitting a curve to the measured signals. Simulation results show that, similar to the genetic algorithm (GA), the proposed CFA technique is far less susceptible to the experimental noise than the CSA. Furthermore, only three measurements of feedback signals are enough for CFA to fit the optimal phase while obtaining a higher focal intensity than the CSA and the GA, dramatically shortening the optimization time by a factor of 3 compared with the CSA and the GA. The proposed CFA approach can be applied to enhance the focus intensity and boost the focusing speed in the fields of biological imaging, particle trapping, laser therapy, and so on, and might help to focus light through dynamic turbid media.

Polarization-selective dynamically tunable multispectral Fano resonances: decomposing of subgroup plasmonic resonances

We analyze the design of near infrared all-optical controllable and dynamically tunable multispectral Fano resonances based on subgroup decomposition of plasmonic resonances in hybrid nanoslits antenna plasmonic system. The theoretical investigation complemented with numerical simulations show that the Fano resonance lines shape can be tailored efficiently and continuously with the nanoslits geometry and the variation of the polarization states of the incident light. The subgroup decomposition of the spectral profile and the modification of plasmonic resonances lineshape that leads to the Fano-type profile of transmission is investigated and revealed. The separate contribution from individual spectral of single-slit array subgroup is attributed to the resulting overall multispectral Fano lineshape of the proposed T-shaped slits array at their corresponding spectral peaks zone. The polarization-selective tunability of the multispectral Fano resonances in the planar hybrid plasmonic system creates new avenues for designing multi-channel multi-wavelength tunable Fano effect.

Exploiting adaptive total variation model for image reconstruction from speckle patterns

Due to the multiple scattering of light in turbid media such as biological tissues, the image of target becomes highly deteriorated even disappears entirely. The adaptive total variation (ATV) image reconstruction algorithm, which is based on majorization-minimization approach together with Bayesian framework, is utilized to recover the object from its speckle pattern. Numerical simulation results indicates that, compared with Tikhonov regularization method, the ATV approach can effectively suppress the noise of the restored image and preserve more image details as well, consequently greatly boosts the SNR and the sharpness of the result image. Furthermore, the recovered results by ATV algorithm have overcome the diffraction-limit of the conventional optical system. Consequently, the combination of ATV algorithm with multiple scattering of turbid media will be beneficial to the observation of cells and protein molecules in biological tissues and other structures in micro/nano scale.