Morphology and statistics of wide-spectrum speckles.

Although the theory of scattered speckles was initially established via idealization of treating the incident light as monochromatic, phenomenon and regulations of wide-spectrum speckles are yet urgent to be studied, with immense growing applications of broadband source such as femtosecond laser, light-emitting-diode and sunlight illumination. Here we quantitatively analyze the morphology and statistics of speckles produced by a point-like source with wide-spectrum, using a phase plate model to describe the scattering layer. Due to differences in induced phase related to wavelength, wide-spectrum speckle patterns appear radial divergence in intensity distribution, as well as in visibility of both speckles and that of the second-order coherence. This is significantly different from the translation-invariance of monochromatic speckles. The spatially-varying morphology and statistics of the speckles contain spatial and spectral information of the incidence, thus can be used as an indicator to achieve optical metrology or sensing with a wide-spectrum source in the scattering environment.

Enhancing robustness of ghost imaging against environment noise via cross-correlation in time domain.

Research towards practical applications of ghost imaging attracts more and more attention in recent years. Signal-to-noise ratio (SNR) of bucket results thus quality of images can be greatly affected by environmental noise, such as strong background light. We introduce temporal cross-correlation into typical ghost imaging to improve SNR of bucket value, taking temporal profile of illumination pulses as a prior information. Experimental results at sunny noontime verified our method, with the imaging quality greatly improved for the object at a distance of 1.3km. We also show the possibility of 3-dimensional imaging, experimentally.

Influence of pulse characteristics on ghost imaging lidar system.

A pulsed pseudo-thermal light source obtained using a rotating ground glass disk, spatial light modulator, or digital micromirror device is widely used in a ghost imaging (GI) lidar system. The property of the pulsed pseudothermal light field determines the reconstruction quality of the image in the GI lidar system, which depends on the pulse extinction ratio (PER) and pulse duty ratio. In this paper, pseudo-thermal light fields obtained at different pulse characteristics are given, taking into account the influence of the exposure time of the charge-coupled device (CCD) camera. The statistical distribution, contrast, and normalized intensity correlated function of the pseudo-thermal light field at different pulse characteristics are analyzed quantitatively for what we believe is the first time. Then the peak signal-to-noise ratio of the reconstructed image using a GI algorithm and a differential ghost imaging (DGI) algorithm is numerically simulated. The simulation results demonstrate that the PSNR decreases as the PER decreases, which is affected by the pulse duty ratio and the CCD exposure time. The deterioration of the reconstruction quality can be reduced by using a DGI algorithm or by shorting the exposure time of the CCD in the GI lidar system.

Denoising ghost imaging under a small sampling rate via deep learning for tracking and imaging moving objects.

Ghost imaging (GI) usually requires a large number of samplings, which limit the performance especially when dealing with moving objects. We investigated a deep learning method for GI, and the results show that it can enhance the quality of images with the sampling rate even down to 3.7%. With a convolutional denoising auto-encoder network trained with numerical data, blurry images from few samplings can be denoised. Then those outputs are used to reconstruct both the trajectory and clear image of the moving object via cross-correlation based GI, with the number of required samplings reduced by two-thirds.

Image recovery of ghost imaging with sparse spatial frequencies.

When the spatial frequencies of the object are insufficiently sampled, the reconstruction of ghost imaging will suffer from repetitive visual artifacts, which cannot be effectively tackled by existing ghost imaging reconstruction techniques. In this Letter, extensions of the CLEAN algorithm applied in ghost imaging are explored to eliminate those artifacts. Combined with the point spread function estimation using the second-order coherence measurement in ghost imaging, our modified CLEAN algorithm is demonstrated to have a fast and noteworthy improvement against the spatial-frequency insufficiency, even for the extreme sparse sampling cases. A brief explanation of the algorithm and performance analysis are given.

Gradual ghost imaging of moving objects by tracking based on cross correlation.

The requirement of a large number of samplings limits the performance of ghost imaging for moving objects. Conventionally, tracking and imaging of the moving objects are done independently; thus, sequential clear images of the moving target during its evolution are required. In this Letter, we propose to obtain the displacement of the object via cross correlation between sequential unclear rough images. Then, a high-quality image of the moving object can be reconstructed gradually during its evolution. Our method works well for translating and rotating objects.

Tracking and imaging of moving objects with temporal intensity difference correlation.

At present, a large number of samplings are required to reconstruct an image of the objects in ghost imaging. When imaging moving objects, it will be hard to perform enough samplings during the moment when the objects can be taken as immobile, causing the reconstructed image of the objects deteriorating. In this paper, we propose a temporal intensity difference correlation ghost imaging scheme, in which a high-quality image of the moving objects within a complex scene can be extracted with much fewer samplings. The spatial sparsity of the moving objects is utilized, while only a linear algorithm is required. This method decreases the number of required samplings, thus relaxing the requirement on high refresh frequency of illumination source and high speed detector, to obtain the information of moving objects with ghost imaging.

Ghost imaging utilizing experimentally acquired degree of linear polarization with no prior information.

Ghost imaging is developed to obtain images of the objects based on intensity correlation of illumination patterns. However, it can be hard to distinguish between objects if the difference between their reflectivities is small. Considering the difference between degrees of polarization in the reflected light from different points, we put forward a method to retrieve distribution of the degree of linear polarization, and obtain high quality image of the objects. With the illumination source being linearly polarized, two orthogonal polarization components of the reflected intensities are measured, from which we can get the distribution of the degree of linear polarization. Furthermore, for the case that the degree of linear polarization can be approximately described with two different values within the field of view, we demonstrate retrieving of the image with high contrast. Our method can be widely applied in different situations, such as extracting the image of target hidden behind disguise or getting higher contrast in bio-imaging.

Ghost imaging normalized by second-order coherence.

Towards improvements in the quality of reconstructed images, the errors in the point spread function of a ghost imaging system caused by a limited number of samplings and imperfect illumination are discussed. We propose an algorithm by normalizing with the second-order coherence of the illumination field, with which the errors caused by imperfect illumination can be reduced, such as non-uniform spatial distribution of the average intensity, spatially varying profile of the second-order degree of coherence, or power fluctuation.

Imaging through scattering layers exceeding memory effect range with spatial-correlation-achieved point-spread-function.

We propose to measure intensity transmission matrices or point-spread-function (PSF) of diffusers via spatial-correlation, with no scanning or interferometric detection required. With the measured PSF, we report optical imaging based on the memory effect that allows tracking of moving objects through a scattering medium. Our technique enlarges the limited effective range of traditional imaging techniques based on the memory effect, and substitutes time-consuming iterative algorithms by a fast cross-correlation deconvolution method to greatly reduce time consumption for image reconstruction.

Multi-scale Adaptive Computational Ghost Imaging.

In some cases of imaging, wide spatial range and high spatial resolution are both required, which requests high performance of detection devices and huge resource consumption for data processing. We propose and demonstrate a multi-scale adaptive imaging method based on the idea of computational ghost imaging, which can obtain a rough outline of the whole scene with a wide range then accordingly find out the interested parts and achieve high-resolution details of those parts, by controlling the field of view and the transverse coherence width of the pseudo-thermal field illuminated on the scene with a spatial light modulator. Compared to typical ghost imaging, the resource consumption can be dramatically reduced using our scheme.

Sub-Rayleigh-diffraction imaging via modulating classical light.

The spatial resolution of a traditional imaging system is restricted by the Rayleigh diffraction limit. In this paper, two types of classical light sources are generated by modulating the amplitude distribution and wavefront of a laser beam randomly, and the generated light sources can exhibit the features of the superposition of two-photon Fock states and the incoherent mixture of two-photon Fock states, respectively. With the generated light sources, the two-fold coherent and incoherent imaging schemes can be achieved, which lead to spatial resolution enhancement, and exceed the Rayleigh diffraction limit in the imaging system.

Visibility properties of the interferometric optical fiber sensors using polarization scrambling.

An interferometric optical fiber sensor relies on the coherent mixing of the optical signals, which is strongly polarization dependent. Random fluctuations in the input state of polarization and the polarization properties of the optical fiber sensor can result in the variation of the visibility and signal fading. Polarization scrambling is an important method to eliminate the input-polarization-induced fading. In this paper, the principles of the polarization scrambling are introduced. The influences of the perturbation to the input fiber and interferometer on the visibility are analyzed in two cases and the visibility properties in an interferometric optical fiber sensor are theoretically and experimentally demonstrated.