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Ghost imaging is a technique for indirectly reconstructing images by utilizing the second-order or higher-order correlation properties of the light field, which exhibits a robust ability to resist interference. On the premise of ensuring the quality of the image, effectively broadening the imaging range can improve the practicality of the technology. In this paper, a dual-path computational ghost imaging method based on convolutional neural networks is proposed. By using the dual-path detection structure, a wider range of target image information can be obtained, and the imaging range can be expanded. In this paper, for the first time, we try to use the two-channel probe as the input of the convolutional neural network and successfully reconstruct the target image. In addition, the network model incorporates a self-attention mechanism, which can dynamically adjust the network focus and further improve the reconstruction efficiency. Simulation results show that the method is effective. The method in this paper can effectively broaden the imaging range and provide a new idea for the practical application of ghost imaging technology.

In this paper, we present an orthogonal ghost imaging (OGI) method based on two-dimensional discrete cosine transform (2D-DCT) patterns. Unlike traditional methods that are based on random or sinusoidal patterns, our method relies on structured orthogonal patterns to enhance both image quality and reconstruction speed, outperforming random and sinusoidal-based approaches in terms of reconstruction fidelity and computational efficiency. A new reconstruction formula is derived in our approach. In addition, using a phase-shift illumination pattern technique helps to effectively reduce environmental noise. Simulation and experimental results show that high-quality image reconstruction is achievable even with reduced sampling rates. For instance, using only 30% of the measurements is enough to meet the Peak Signal-to-Noise Ratio (PSNR) threshold predicted by Shannon entropy. Compared to differential and sinusoidal ghost imaging techniques, the proposed method consistently outperforms them in terms of signal-to-noise ratio (SNR) and reconstruction efficiency. These findings suggest that OGI offers a promising direction for efficient and low-cost ghost imaging systems.

Computational ghost imaging, as an alternative photoelectric imaging technology, uses a single-pixel detector with no spatial resolution to capture information and reconstruct the image of a scene. Due to its essentially temporal measurement manner, improving the image frame rate is always a major concern in the research of computational ghost imaging technology. By taking advantage of the fast switching time of LED, an LED array was developed to provide a structured illumination light source in our work, which significantly improves the structured illumination rate in the computational ghost imaging system. The design of the LED array driver circuit presented in this work makes full use of the LED switching time and achieves a pattern displaying rate of 12.5 MHz. Continuous images with 32 × 32 pixel resolution are reconstructed at a frame rate of 25,000 fps, which is approximately 500 times faster than what a universally used digital micromirror device can achieve. The LED array presented in this work can potentially be applied to other techniques requiring high-speed structured illumination, such as fringe 3D profiling and array-based LIFI.

In this study, we propose an attention-enhanced computational ghost imaging method (AEGI). AEGI integrates the attention mechanism into the framework of computational ghost imaging. In the process of image reconstruction, the attention mechanism identifies and captures object-relevant information from the extracted features, constrained by the discrepancy between the 1D intensity of the predicted and actual object image. This process can adjust the weight of the feature by exploring the relationship between the feature and adjacent features, thus enhancing the object signal while suppressing background noise in the final image. In addition, we have conducted some experiments in unfamiliar space and underwater environments to verify the effectiveness of AEGI. The results show that AEGI can reconstruct object images with high quality, which greatly enhances the practical application capabilities of computational ghost imaging.

Unlike traditional optical imaging schemes, computational ghost imaging (CGI) provides a way to reconstruct images with the spatial distribution information of illumination patterns and the light intensity collected by a single-pixel detector or bucket detector. Compared with stationary scenes, the relative motion between the target and the imaging system in a dynamic scene causes the degradation of reconstructed images. Therefore, we propose a time-variant retina-like computational ghost imaging method for axially moving targets. The illuminated patterns are specially designed with retina-like structures, and the radii of foveal region can be modified according to the axial movement of target. By using the time-variant retina-like patterns and compressive sensing algorithms, high-quality imaging results are obtained. Experimental verification has shown its effectiveness in improving the reconstruction quality of axially moving targets. The proposed method retains the inherent merits of CGI and provides a useful reference for high-quality GI reconstruction of a moving target.

Single-pixel imaging techniques extend the time dimension to reconstruct a target scene in the spatial domain based on single-pixel detectors. Structured light illumination modulates the target scene by utilizing multi-pattern projection, and the reflected or transmitted light is measured by a single-pixel detector as total intensity. To reduce the imaging time and capture high-quality images with a single-pixel imaging technique, orthogonal patterns have been used instead of random patterns in recent years. The most representative among them are Hadamard patterns and Fourier sinusoidal patterns. Here, we present an alternative Fourier single-pixel imaging technique that can reconstruct high-quality images with an intensity correlation algorithm using acquired Fourier positive–negative images. We use the Fourier matrix to generate sinusoidal and phase-shifting sinusoid-modulated structural illumination patterns, which correspond to Fourier negative imaging and positive imaging, respectively. The proposed technique can obtain two centrosymmetric images in the intermediate imaging course. A high-quality image is reconstructed by applying intensity correlation to the negative and positive images for phase compensation. We performed simulations and experiments, which obtained high-quality images, demonstrating the feasibility of the methods. The proposed technique has the potential to image under sub-sampling conditions.

Ghost imaging (GI) is an imaging modality typically based on correlations between a single-pixel (bucket) detector collecting the electromagnetic field which was transmitted through or reflected from an object and a high-resolution detector which measures the field that did not interact with the object. When using partially coherent sources, fluctuations can be introduced into a beam by rotating or translating a diffuser, and then the beam is split into two beams with identical intensity fluctuations. In computational GI, the diffuser with an unknown scatter distribution is replaced by a diffuser with a known scatter distribution so that the reference beam and high-resolution detector can be discarded. In this work, we wish to examine how the relation between the diffuser’s autocorrelation length and its spatial displacement affects the quality of image reconstruction obtained with these methods. We first analyze this general question theoretically and simulatively, and we then present some specific, proof-of-principle results we obtained in an optical setup. Finally, we discuss the relation between theory and experiment, suggesting some general conclusions regarding the preferred working points.

In existing cryptographic key distribution (CKD) protocols based on computational ghost imaging (CGI), the interaction among multiple legitimate users is generally neglected, and the channel noise has a serious impact on the performance. To overcome these shortcomings, we propose a multi-party interactive CKD protocol over a public network, which takes advantage of the cascade ablation of fragment patterns (FPs). The server splits a quick-response (QR) code image into multiple FPs and embeds different “watermark” labels into these FPs. By using a CGI setup, the server will acquire a series of bucket value sequences with respect to different FPs and send them to multiple legitimate users through a public network. The users reconstruct the FPs and determine whether there is an attack in the public channel according to the content of the recovered “watermark” labels, so as to complete the self-authentication. Finally, these users can extract their cryptographic keys by scanning the QR code (the cascade ablation result of FPs) returned by an intermediary. Both simulation and experimental results have verified the feasibility of this protocol. The impacts of different attacks and the noise robustness have also been investigated.

To achieve high capacity, high speed, and secure image transmission, we propose a multi-image computational ghost imaging (CGI)-based encryption scheme that integrates compressed sensing (CS), block scrambling, and dynamic-salt-driven bidirectional XOR diffusion. First, multiple images are partitioned into 8 × 8 pixel blocks, and their spatial structure is disrupted through random scrambling. The scrambled composite image then undergoes pixel-level encryption via two-round bidirectional XOR diffusion, using session-unique keys derived from SHA-256-based dynamic salt, eliminating the statistical characteristics of the original images. Subsequently, each pixel block is subjected to both Gaussian CS and Hadamard-based CGI measurements in parallel, achieving dual-mode compressive encryption and enhancing robustness through measurement redundancy. Finally, only the scrambling key, the XOR-diffusion key, and the compressed measurements are stored; the original image information is thus transformed into unrecognizable measurement data. During the decryption process, the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) with a Discrete Cosine Transform (DCT) sparse basis is employed for dual-sparse reconstruction from the compressed measurements, recovering the encrypted composite image. An inverse XOR operation is then applied to remove the pixel-level diffusion, followed by block reordering using the scrambling key to restore the original images. Experimental results demonstrate that the proposed scheme enables efficient and secure multi-image transmission while maintaining high decrypted image quality. Security analysis indicates that the scheme possesses high key sensitivity, effectively resisting chosen-plaintext attacks. Histogram uniformity analysis and cropping attack resistance experiments further confirm its excellent statistical security and robustness.

To enhance imaging quality, singular value decomposition (SVD) has been applied to single-wavelength ghost imaging (GI) or color GI. In this paper, we extend the application of SVD to wavelength-multiplexing ghost imaging (WMGI) for reducing the redundant information in the random measurement matrix corresponding to multi-wavelength modulated speckle fields. The feasibility of this method is demonstrated through numerical simulations and optical experiments. Based on the intensity statistical properties of multi-wavelength speckle fields, we derived an expression for the contrast-to-noise ratio (CNR) to characterize imaging quality and conducted a corresponding analysis. The theoretical results indicate that in SVDWMGI, for the m-wavelength case, the CNR of the reconstructed image is m times that of single-wavelength GI. Moreover, we carried out an optical experiment with a three-wavelength speckle-modulated light source to verify the method. This approach integrates the advantages of both SVD and wavelength division multiplexing, potentially facilitating the application of GI in long-distance imaging fields such as remote sensing.