Compressive Ghost Imaging of the Moving Object Using the Low-Order Moments

Prior-image-filtered ghost imaging

In this study, a novel precise reconstruction method was proposed for ghost imaging. In traditional ghost imaging (TGI), image quality deteriorates in proportion to the ℓ2 norm of the observed object. However, the proposed method reduces the effective ℓ2 norm by filtering an unknown direct current component and an arbitrary alternating current component derived from a pre-measured rough image. Mathematical analysis demonstrated that the proposed method outperformed TGI in terms of precision. Numerical simulations and experiments validated that the mean squared error in the proposed method was considerably lower than that in existing online algorithms, demonstrating its effectiveness for high-resolution single-pixel imaging.

Single-pixel imaging robust to arbitrary translational motion

Single-pixel imaging (SPI) stands out in computational imaging for its simplicity and adaptability, yet its performance has been hampered by artifacts from translational motion. Existing solutions heavily rely on accurate motion modeling, requiring additional hardware and computational costs. In this Letter, we propose translational motion-agnostic SPI (TMA-SPI), a novel, to the best of our knowledge, single-object SPI framework agnostic to arbitrary translational motion. Our dual-domain optimization method leverages the translation invariance property of the amplitude spectrum in the Fourier domain, combined with the spatially finite and nonnegative constraints in the image domain, to produce a clear image of the moving object without any motion estimation or compensation. Through both simulation and the deployment of a real imaging prototype, we demonstrate its superior performance over the conventional SPI method. Our framework is expected to extend the applicability of SPI, offering significant improvements for dynamic sensing applications.

A W-Shaped Self-Supervised Computational Ghost Imaging Restoration Method for Occluded Targets

We developed a novel method based on self-supervised learning to improve the ghost imaging of occluded objects. In particular, we introduced a W-shaped neural network to preprocess the input image and enhance the overall quality and efficiency of the reconstruction method. We verified the superiority of our W-shaped self-supervised computational ghost imaging (WSCGI) method through numerical simulations and experimental validations. Our results underscore the potential of self-supervised learning in advancing ghost imaging.

Three-dimensional quantum imaging of dynamic targets using quantum compressed sensing

Quantum imaging based on entangled light sources exhibits enhanced background resistance compared to conventional imaging techniques in low-light conditions. However, direct imaging of dynamic targets remains challenging due to the limited count rate of entangled photons. In this paper, we propose a quantum imaging method based on quantum compressed sensing that leverages the strong correlation characteristics of entangled photons and the randomness inherent in photon pair generation and detection. This approach enables the construction of a compressed sensing system capable of directly imaging high-speed dynamic targets. The results demonstrate that our system successfully achieves imaging of a target rotating at a frequency of 10 kHz, while maintaining an impressive data compression rate of 10-6. This proposed method introduces a pioneering approach for the practical implementation of quantum imaging in real-world scenarios.

Fast focusing method in ghost imaging with a tracking trajectory

The imaging environment is unstable for trembling disturbance, which is detrimental to object reconstruction. In this Letter, we experimentally investigated ghost imaging (GI) under a temporal trembling disturbance. The fast-focusing method based on imaging with small sampling measurements is proposed, and the theoretical model and algorithm are validated. It is demonstrated that the proposed method is effective to obtain a better-resolution image of the object under the strong trembling disturbance including a laboratory and a real trembling environment. The results provide a promising approach to deal with image degradation caused by an unstable environment and can find potential applications for ghost imaging in remote sensing.

Advances in Ghost Imaging of Moving Targets: A Review

Ghost imaging is a novel imaging technique that utilizes the intensity correlation property of an optical field to retrieve information of the scene being measured. Due to the advantages of simple structure, high detection efficiency, etc., ghost imaging exhibits broad application prospects in the fields of space remote sensing, optical encryption transmission, medical imaging, and so on. At present, ghost imaging is gradually developing toward practicality, in which ghost imaging of moving targets is becoming a much-needed breakthrough link. At this stage, we can improve the imaging speed and improve the imaging quality to seek a more optimized ghost imaging scheme for moving targets. Based on the principle of moving target ghost imaging, this review summarizes and compares the existing methods for ghost imaging of moving targets. It also discusses the research direction and the technical challenges at the current stage to provide references for further promotion of the instantiation of ghost imaging applications.

Single-pixel imaging of a translational object

Image-free tracking methods based on single-pixel detectors (SPDs) can track a moving object at a very high frame rate, but they rarely can achieve simultaneous imaging of such an object. In this study, we propose a method for simultaneously obtaining the relative displacements and images of a translational object. Four binary Fourier patterns and two differential Hadamard patterns are used to modulate one frame of the object and then modulated light signals are obtained by SPD. The relative displacements and image of the moving object can be gradually obtained along with the detection. The proposed method does not require any prior knowledge of the object and its motion. The method has been verified by simulations and experiments, achieving a frame rate of 3332 Hz to acquire relative displacements of a translational object at a spatial resolution of 128 × 128 pixels using a 20000-Hz digital micro-mirror device. This proposed method can broaden the application of image-free tracking methods and obtain spatial information about moving objects.

Multiple-Image Reconstruction of a Fast Periodic Moving/State-Changed Object Based on Compressive Ghost Imaging

We propose a multiple-image reconstruction scheme of a fast periodic moving/state-changed object with a slow bucket detector based on compressive ghost imaging, named MIPO-CSGI. To obtain N frames of an object with fast periodic moving/state-changed, N random speckle patterns are generated in each cycle of the object, which are then used to illuminate the object one by one. The total energy reflected from the object is recorded by a slow bucket detector at each cycle time T. Each group with N random speckle patterns is programmed as one row of a random matrix, and each row of the matrix element corresponds to one measurement of the slow bucket detector. Finally, the compressive sensing algorithm is applied to the constructed matrix and bucket detector signals, resulting in the direct acquisition of multiple images of the object. The feasibility of our method has been demonstrated in both numerical simulations and experiments. Hence, even with a slow bucket detector, MIPO-CSGI can image a fast periodic moving/state-changed object effectively.

Motion Deblurring for Single-Pixel Spatial Frequency Domain Imaging

The single-pixel imaging technique is applied to spatial frequency domain imaging (SFDI) to bring significant performance advantages in band extension and sensitivity enhancement. However, the large number of samplings required can cause severe quality degradations in the measured image when imaging a moving target. This work presents a novel method of motion deblurring for single-pixel SFDI. In this method, the Fourier coefficients of the reflected image are measured by the Fourier single-pixel imaging technique. On this basis, a motion-degradation-model-based compensation, which is derived by the phase-shift and frequency-shift properties of Fourier transform, is adopted to eliminate the effects of target displacements on the measurements. The target displacements required in the method are obtained using a fast motion estimation approach. A series of numerical and experimental validations show that the proposed method can effectively deblur the moving targets and accordingly improves the accuracy of the extracted optical properties, rendering it a potentially powerful way of broadening the clinical application of single-pixel SFDI.