Several common reconstruction algorithms were used to perform compressive quantum imaging with Fock states, coherent states, and squeezed light. It was found that the different light sources gave the same scaling law for the root-mean-squared error.
Compressive Imaging has been an extensively researched area inoptical imaging, object tracking,satellite applications,etc. There are many signalrecovery methods and comparative analyses of different algorithms in the presence of Gaussian noise. However, certain applications such as optical imaging at low photon intensityhavecounts of discrete events, which cannot be modelled using a Gaussian noise model. Instead, anoise model that incorporates photon statistics is needed. Researchers haveworkedon the Poisson noise modeland a different compressive sensing reconstruction was found.In this thesis, weconsidered a more general scenario of Compressive Imaging using non-classical photon states as light sources. We assumed that the Compressive Imaging system that consists of digital micromirror device (DMD), lenses, and detectors are perfect so that all noises comes from photons. Fock states and squeezed light thatpossess non-Poissonian statistics plays animportant role in Quantum Imaging. The image reconstruction was performed using several common compressive sensing signal reconstruction algorithms assuming Gaussian noise. This thesis showedthe behavior of the root mean square error (RMSE)with respectto the signal-to-noise ratio (SNR)for different photon statistics. In particular, the study showedthat all the noises perform similarly for the different algorithms. Based on the performance results for the different light sources, this researchcan be helpful in designing ageneralized Compressive Sensing model incorporating the photon statistics thatareapplicable in the field of Quantum Optics.
This work experimentally demonstrates that the imaging quality of quantum ghost imaging (GI) with entangled photons can be significantly improved by properly handling the errors caused by the imperfection of optical devices. We also consider compressive GI to reduce the number of measurements and thereby the data acquisition time. The image reconstruction is formulated as a sparse total least square problem which is solved with an iterative algorithm. Our experiments show that, compared with existing methods, the new method can achieve a significant performance gain in terms of mean square error and peak signal⁻noise ratio.
Aided by quantum sources, quantum metrology helps enhance measurement precision. Here, we construct a theoretical model for quantum imaging based on squeezed states and present the corresponding numerical results. Through discretization and quantum Fisher information theory, we investigate the two-point resolution and spatial multi-parameter estimation of optical fields with unknown spatial distributions. We calculate and compare imaging results based on squeezed vacuum states, coherent states, and squeezed coherent states; our results show that squeezed coherent states yield greater quantum Fisher information, which can effectively improve imaging quality. In addition, we analyze the influence of imaging basis functions, degree of squeezing, quantum correlations, and other factors on imaging performance. The proposed quantum imaging model and computational method can be extended to more complex scenarios, such as multi-mode squeezed-state imaging schemes and incoherent imaging systems. In the future, it is expected to find applications in practical imaging systems, including Raman microscopy and stimulated Brillouin scattering imaging.
Quantum imaging (QI) has some unique advantages, such as nonlocal imaging and enhanced space resolution. However, the quality of the reconstructed images and the time of data acquisition leave much to be desired. Based on the framework of compressive sensing, we propose an optimization criterion for high-quality QI whereby total variation restriction is specifically utilized for noise suppression. The corresponding reported algorithm uses a combination of a greedy strategy and the interactive reweight least-squares method. The simulation and the actual imaging experiment both show a significant improvement of the proposed algorithm the over previous imaging method.
Nonlocal point-to-point correlations between two photons have been used to produce ``ghost'' images without placing the camera towards the object. Here we theoretically demonstrate and analyze the advantage of non-Gaussian quantum light in improving the image quality of a ghost imaging system over a traditional Gaussian light source. For any squeezing degree, the signal-to-noise ratio (SNR) of the ghost image can be enhanced by the non-Gaussian operations of photon addition and subtraction on a two-mode squeezed light source. We find striking evidence that using non-Gaussian coherent operations, the SNR can be promoted to a high level even within the extremely weak squeezing regime. The resulting insight provides experimental recipes of quantum imaging using non-Gaussian light for illumination.