Ghost imaging LiDAR via sparsity constraints using push-broom scanning.

Ghost imaging LiDAR via sparsity constraints using push-broom scanning is proposed. It can image the stationary target scene continuously along the scanning direction by taking advantage of the relative movement between the platform and the target scene. Compared to conventional ghost imaging LiDAR that requires multiple speckle patterns staring the target, ghost imaging LiDAR via sparsity constraints using push-broom scanning not only simplifies the imaging system, but also reduces the sampling number. Numerical simulations and experiments have demonstrated its efficiency.

Hyperspectral ghost imaging camera based on a flat-field grating.

A spectral camera based on ghost imaging via sparsity constraints (GISC) acquires a three-dimensional (3D) spatial-spectral data cube of the target through a two-dimensional (2D) detector in a single snapshot. However, the spectral and spatial resolution are interrelated because both of them are modulated by the same spatial random phase modulator. In this paper, we theoretically and experimentally demonstrate a system by equipping the GISC spectral camera with a flat-field grating to disperse the light fields before the spatial random phase modulator, hence consequently decoupling the spatial and spectral resolution. By theoretical derivation of the imaging process we obtain the spectral resolution 1nm and spatial resolution 50µm about the new system which are verified by the experiment. The new system can not only modulate the spatial and spectral resolution separately, but also provide a possibility of optimizing the light field fluctuations of different wavelengths according to the imaging scene.

Structured image reconstruction for three-dimensional ghost imaging lidar.

A structured image reconstruction method has been proposed to obtain high quality images in three-dimensional ghost imaging lidar. By considering the spatial structure relationship between recovered images of scene slices at different longitudinal distances, orthogonality constraint has been incorporated to reconstruct the three-dimensional scenes in remote sensing. Numerical simulations have been performed to demonstrate that scene slices with various sparse ratios can be recovered more accurately by applying orthogonality constraint, and the enhancement is significant especially for ghost imaging with less measurements. A simulated three-dimensional city scene has been successfully reconstructed by using structured image reconstruction in three-dimensional ghost imaging lidar.

Morphology separation in ghost imaging via sparsity constraint.

The separation of morphology components in ghost imaging via sparsity constraint is investigated by adapting the morphology component analysis technique based on the fact that different morphology components can be sparsely expressed in different basis. The successful separation of reconstructed image plays an important role in the ability to identify it, analyze it, enhance it and more. This approach is first studied with numerical simulations and then verified with both table-top and outdoor experimental data. Results show that it can not only separate different morphology components but also improve the quality of the reconstructed image.

Application of multi-correlation-scale measurement matrices in ghost imaging via sparsity constraints.

Sampling and reconstruction techniques are of special interest and importance in ghost imaging. Up to now, the transverse correlation scale of measurement matrices are usually constant. This paper explores a new possibility of constructing highly efficient measurement matrices with multi-correlation scales. Comparisons between the simulational and experimental results show that the multi-correlation-scale measurement matrices are highly efficient and accurate in sampling and image reconstruction and have a better antinoise ability than the existing constant-correlation-scale measurement matrices.

Experimental investigation of the quality of ghost imaging via sparsity constraints.

Sampling number and detection signal-to-noise ratio (SNR) are two major factors influencing imaging quality. Combining the image's sparsity in the representation basis with the ghost imaging (GI) approach, GI via sparsity constraints (GISC) can nonlocally image the object even when the measurement number is far fewer than the Nyquist criteria required for the conventional GI reconstruction algorithm. The influence of receiving the system's numerical aperture and detection SNR in the test path to GISC is studied through experiments. It is also shown that the quality of GISC depends on the object's sparse representation basis.

One-pot tuning of Au nucleation and growth: from nanoclusters to nanoparticles.

We describe a simple and effective method to obtain colloidal surface-functionalized Au nanoparticles. The method is primarily based on irradiation of a gold solution with high-flux X-rays from a synchrotron source in the presence of 11-mercaptoundecanoic acid (MUA). Extensive tests of the products demonstrated high colloidal density as well as excellent stability, shelf life, and biocompatibility. Specific tests with X-ray diffraction, UV-visible spectrometry, visible microscopy, Fourier transform infrared spectroscopy, dark-field visible-light scattering microscopy, and transmission electron microscopy demonstrated that MUA, being an effective surfactant, not only allows tunable size control of the nanoparticles, but also facilitates functionalization. The nanoparticle sizes were 6.45 ± 1.58, 1.83 ± 1.21, 1.52 ± 0.37 and 1.18 ± 0.26 nm with no MUA and with MUA-to-Au ratios of 1:2, 1:1, and 3:1. The MUA additionally enabled functionalization with l-glycine. We thus demonstrated flexibility in controlling the nanoparticle size over a large range with narrow size distribution.

Investigation of misalignment in analyzer crystal based-CT and its effect.

Analyzer crystal-based computed tomography (ACB-CT) is a novel x-ray phase contrast computed tomography technique. In this paper, in order to extract the refraction angle, we analyzed the effect of misalignment on tomography-sliced images of ACB-CT. Two different methods were considered: the first, proposed by Chapman and Dilmanian, was based on two sets of projective image data taken at both sides of the rocking curve and with a half circle sample rotation and the second, recently proposed by our team, was based on only one set of projective image data taken at one side of the rocking curve but with a full circle sample rotation. Theoretical analysis and experimental results will show that the second method improves the quality of reconstructed CT images, also simplifying the ACB-CT data acquisition procedure.

Spectral Camera based on Ghost Imaging via Sparsity Constraints.

The image information acquisition ability of a conventional camera is usually much lower than the Shannon Limit since it does not make use of the correlation between pixels of image data. Applying a random phase modulator to code the spectral images and combining with compressive sensing (CS) theory, a spectral camera based on true thermal light ghost imaging via sparsity constraints (GISC spectral camera) is proposed and demonstrated experimentally. GISC spectral camera can acquire the information at a rate significantly below the Nyquist rate, and the resolution of the cells in the three-dimensional (3D) spectral images data-cube can be achieved with a two-dimensional (2D) detector in a single exposure. For the first time, GISC spectral camera opens the way of approaching the Shannon Limit determined by Information Theory in optical imaging instruments.

Phase retrieval from a single near-field diffraction pattern with a large Fresnel number.

A new method of phase retrieval from a single near-field diffraction image with a large Fresnel number is presented and discussed. This method requires only the oversampled diffraction pattern without any other information such as the object envelope. Moreover, we show that the combination with a fast computational method is possible when the linear oversampling ratio is an integer. Numerical simulations are also presented, showing that the method works well with noisy data.