Defogging computational ghost imaging via eliminating photon number fluctuation and a cycle generative adversarial network

Imaging through fluctuating scattering media such as fog is of challenge since it seriously degrades the image quality.We investigate how the image quality of computational ghost imaging is reduced by fluctuating fog and how to obtain a high-quality defogging ghost image. We show theoretically and experimentally that the photon number fluctuations introduced by fluctuating fog is the reason for ghost image degradation. An algorithm is proposed to process the signals collected by the computational ghost imaging device to eliminate photon number fluctuations of different measurement events. Thus, a high-quality defogging ghost image is reconstructed even though fog is evenly distributed on the optical path. A nearly 100% defogging ghost image is obtained by further using a cycle generative adversarial network to process the reconstructed defogging image.

Phase modulation pseudocolor encoding ghost imaging

We present a ghost imaging scheme that can obtain a good pseudocolor image of black-and-white objects.The essential idea is to use a multi-wavelength thermal light source and the phase modulation pseudocolor encoding technique,which overcomes the disadvantages of other methods involved spatial filtering.Therefore,the pseudocolor ghost image achieved by this imaging scheme is better than that obtained by other methods in terms of brightness,color,and signal-tonoise ratio.

Ghost imaging with broad distance

We present a scheme that is able to achieve the ghost imaging with broad distance. The physical nature of our scheme is that the different wavelength beams are separated in free space by an optical media according to the slow light or dispersion principle. Meanwhile, the equality of the optical distance of the two light arms is not violated. The photon correlation is achieved by the rotating ground glass plate(RGGP) and spatial light modulator(SLM), respectively. Our work shows that a monochromic ghost image can be obtained in the case of RGGP. More importantly, the position(or distance) of the object can be ascertained by the color of the image. Thus, the imaging and ranging processes are combined as one process for the first time to the best of our knowledge. In the case of SLM, we can obtain a colored image regardless of where the object is.

量子非定域衍射实验

基于量子成像技术和衍射现象设计了验证量子非定域衍射实验.使用LabVIEW程序控制桶探测器和CCD相机同时拍照,使用Matlab程序对桶探测器拍的图像和CCD相机拍的图像求和运算,得到量子非定域衍射图像.

Optimizing quantum correlation dynamics by weak measurement in dissipative environment

We investigate the protection of quantum correlations of two qubits in independent vacuum reservoirs by means of weak measurements. It is found that the weak measurement can reduce the amount of quantum correlation for one type of initial state at the beginning in a non-Markovian environment and meanwhile it can reduce the occurrence time of entanglement sudden death(ESD) in the process of time evolution. In a Markovian environment, the quantum entanglements of the two kinds of initial states decay rapidly and the weak measurement can further weaken the quantum entanglement,therefore in this case the entanglement cannot be optimized in the evolution process.

Computational ghost imaging with deep compressed sensing

Computational ghost imaging(CGI)provides an elegant framework for indirect imaging,but its application has been restricted by low imaging performance.Herein,we propose a novel approach that significantly improves the imaging performance of CGI.In this scheme,we optimize the conventional CGI data processing algorithm by using a novel compressed sensing(CS)algorithm based on a deep convolution generative adversarial network(DCGAN).CS is used to process the data output by a conventional CGI device.The processed data are trained by a DCGAN to reconstruct the image.Qualitative and quantitative results show that this method significantly improves the quality of reconstructed images by jointly training a generator and the optimization process for reconstruction via meta-learning.Moreover,the background noise can be eliminated well by this method.

基于弱测量操作下的量子关联动力学及关联转移

利用弱测量技术研究两量子比特的量子关联动力学演化和转移。有纠缠突然死亡现象产生的非最大纠缠初态经前选择测量操作后,在演化过程中不但能够提高量子关联强度,而且能够缩短甚至消除量子比特纠缠的突然死亡区域;而对于演化中无纠缠突然死亡的非最大纠缠初态,弱测量操作则会降低量子比特间关联强度。对初始处于最大纠缠态的粒子,弱测量操作不能增强粒子间的纠缠但可以优化量子关联中的量子失谐。弱测量在实现量子关联转移时的破坏极小,能够延迟纠缠的衰减,并可使量子纠缠的转移时间提前。另外腔场与量子比特间的耦合强度与系统耗散率的关系决定纠缠原子在腔中动力学演化,表现为马尔可夫和非马尔可夫过程。

多模差分鬼成像

提出了多模差分鬼成像技术(MWDGI)可以提高鬼成像的信噪比(SNR)。对使用旋转的毛玻璃板和空间光调制系统的两种情况的MWDGI分别做出了理论分析。研究结果表明MWDGI结合了差分鬼成像(DGI)和多模鬼成像(MWGI)的优点,能够显著提高各种类型物体的鬼成像的SNR。

Reflective ghost imaging with classical Gaussian-state light

In this letter,we use quantum description and the Gaussian state to study reflective ghost imaging with two classical sources,and to provide their expressions.We find that the reflective ghost imaging of a roughsurfaced object,using Gaussian-state phase-insensitive or classically correlated phase-sensitive light,can be expressed in terms of the phase-insensitive or phase-sensitive cross-correlations between the two detected fields,including a background term.Moreover,reflective ghost imaging with two classical Gaussian-state lights is shown to have similar features as spatial resolution and field of view.

Computational ghost imaging with compressed sensing based on a convolutional neural network

Computational ghost imaging(CGI)has recently been intensively studied as an indirect imaging technique.However,the image quality of CGI cannot meet the requirements of practical applications.Here,we propose a novel CGI scheme to significantly improve the imaging quality.In our scenario,the conventional CGI data processing algorithm is optimized to a new compressed sensing(CS)algorithm based on a convolutional neural network(CNN).CS is used to process the data collected by a conventional CGI device.Then,the processed data are trained by a CNN to reconstruct the image.The experimental results show that our scheme can produce higher quality images with the same sampling than conventional CGI.Moreover,detailed comparisons between the images reconstructed using the deep learning approach and with conventional CS show that our method outperforms the conventional approach and achieves a ghost image with higher image quality.