Compressive adaptive ghost imaging via sharing mechanism and fellow relationship.

The existing adaptive single-pixel imaging methods suffer from a waste of sampling resources. The sampling resources are not used adequately for superior localization of significant coefficients and reconstruction. In this paper, an adaptive single-pixel imaging method via the guided coefficients in the Haar wavelet tree is proposed. The goal is to achieve high quality imaging with less sampling resources. The guided coefficients are selected from the unsampled coefficients by a proposed same-scale prediction method based on the sampled coefficients. These guided coefficients are used to localize the significant coefficients with higher resolution belonging to the sampled coefficients and the significant coefficients belonging to the guided coefficients by a proposed guided prediction method. The significant guided coefficients are then used in the composite reconstruction method to reconstruct the image. Performance analysis shows that the proposed method reduces waste of the sampling resources and localizes more significant coefficients. Simulation results demonstrate that the proposed method improves the imaging quality in terms of peak signal-to-noise ratio up to 29.7 dB for the images containing regular and chaotic textures in the noise-free environment. The sampling rate for the same imaging quality can be reduced up to 56%. Under the noisy condition, the proposed method also achieves better imaging quality at a lower sampling rate.

The underwater environment is extremely complex and variable, which makes it difficult for underwater robots detecting or recognizing surroundings using images acquired with cameras. Ghost imaging as a new imaging technique has attracted much attention due to its special physical properties and potential for imaging of objects in optically harsh or noisy environments. In this work, we experimentally study three categories of image reconstruction methods of ghost imaging for objects of different transmittance. For high-transmittance objects, the differential ghost imaging is more efficient than traditional ghost imaging. However, for low-transmittance objects, the reconstructed images using traditional ghost imaging and differential ghost imaging algorithms are both exceedingly blurred and cannot be improved by increasing the number of measurements. A compressive sensing method named augmented Lagrangian and alternating direction algorithm (TVAL3) is proposed to reduce the background noise imposed by the low-transmittance. Experimental results show that compressive ghost imaging can dramatically subtract the background noise and enhance the contrast of the image. The relationship between the quality of the reconstructed image and the complexity of object itself is also discussed.

To solve the problem of poor quality in ghost imaging via sparsity constraints (GISC) multispectral image reconstruction with correlation operations and compressed sensing algorithms under low sampling rate detection conditions, we propose an end-to-end deep-learning-based method. Based on the U-Net, Res2Net-SE-Conv is employed instead of convolutional blocks to extract local and global image features at a more fine-grained level while adaptively adjusting the channel feature response. The two-dimensional contextual transformer is constructed to fully use contextual correlation information to enhance the effectiveness of feature representations. We employ the two-dimensional contextual transformer in the decoder part, dubbed CoT-Unet, to reconstruct the desired 3D cube. The results show that compared with U-Net, TSA-Net based on spatial-spectral self-attention, the PSNR of reconstructed images by the CoT-Unet is improved by 5 dB and 3 dB, respectively, SSIM is improved by 0.23 and 0.07, and SAM is decreased by 0.06 and 0.58. Compared with conventional algorithms such as DGI and CS, our method significantly improves the quality of reconstructed images. Furthermore, the comparison results at 10%, 20%, and 30% sampling rates show that our approach has the best quality in reconstructing GISC multispectral images at low sampling rates.

As computational ghost imaging is widely used in the military, radar, and other fields, its security and efficiency became more and more important. In this paper, we propose a compressive ghost imaging encryption scheme based on the hyper-chaotic system, DNA encoding, and KSVD algorithm for the first time. First, a 4-dimensional hyper-chaotic system is used to generate four long pseudorandom sequences and diffuse the sequences with DNA operation to get the phase mask sequence, and then N phase mask matrixes are generated from the sequences. Second, in order to improve the reconstruction efficiency, KSVD algorithm is used to generate dictionary D to sparse the image. The transmission key of the proposed scheme includes the initial values of hyper-chaotic and dictionary D, which has plaintext correlation and big key space. Compared with the existing compressive ghost imaging encryption scheme, the proposed scheme is more sensitive to initial values and more complexity and has smaller transmission key, which makes the encryption scheme more secure, and the reconstruction efficiency is higher too. Simulation results and security analysis demonstrate the good performance of the proposed scheme.

Orthogonal sparse dictionary based on Chirp echo for ultrasound imaging

Compressive ghost imaging (CSGI) combines structured illumination and a bucket detector for obtaining the light intensity signal from an unknown object. Light fluctuations are generated by few measurements and then an image is reconstructed using optimization algorithms such as compressive sensing (CS) by finding its sparse representation. The measured light fluctuations are not sensitive to scattering degradation. Consequently, an absorbing object completely embedded in a scattering media can be imaged. To speed up the sequential loading of illumination patterns on a digital micromirror device and achieve data compression, the sampling number should be reduced because more than 90% of the time in CS reconstruction is usually spent in getting a frame of image. In this study, we propose a novel strategy to realize a speedy and reliable reconstruction procedure for obtaining a high image quality by using prior knowledge during the acquisition of light intensity signal in CSGI. The prior knowledge is established by extracting the features of a local target area, which is designed by descattering of images with extra noise using fast Fourier single-pixel imaging. The proposed method facilitates a reliable image quality even under the reduction in the compression ratio, thereby overcoming the limitation of the dependence of sampling ratio on the image quality in CSGI.

Spectral ghost imaging camera with super-Rayleigh modulator

Single-pixel imaging (SPI) is a computational imaging architecture that utilizes non-resolution detectors to acquire two-dimensional and three-dimensional image information under structured illumination. This method possesses strong anti-interference capability under low-light conditions and has great potential for applications. However, the imaging quality and reconstruction efficiency of SPI remain significant constraints on its practical applications under low sampling conditions. Here, we propose a non-pre-trained complex-valued network-based compressive Fourier SPI technique to obtain a high-performance reconstructed image at a low sampling rate. This technique utilizes a physical model as prior information to achieve network-driven optimization and thus does not need to be pre-trained on any dataset. The performance is demonstrated simultaneously by simulation analysis and experimental validation, showing a peak signal-to-noise ratio of 23.68dB at a sampling rate of 0.05 for 64×64pixel images. The proposed complex domain network-based compressive SPI imaging scheme paves the way for practical applications.

Speckle patterns arise when coherent light reflects from rough surfaces or propagates through complex scattering media, such as disordered materials or multimode fibers. These patterns exhibit a random granular structure, with their statistical properties typically following Rayleigh statistics. The probability density function (PDF) of the complex field conforms to a circular Gaussian distribution, while the intensity PDF follows a negative exponential distribution. Wavefront shaping techniques can modify these statistical properties by controlling wavefront intensity, polarization, or phase [1], enabling diverse applications, including advanced imaging, cryptography, and optical communications. Compressive ghost imaging (CGI) leverages speckle patterns for image reconstruction using a bucket detector instead of a traditional camera [2]. This technique relies on compressive sensing, which exploits object sparsity to achieve accurate image reconstruction with fewer measurements. CGI is particularly advantageous in challenging environments, such as imaging through highly scattering or dynamic media.

With the rapid development of computer technology, information security has attracted more and more attention. It is not only related to the information and property security of individuals and enterprises, but also to the security and social stability of a country. Identity authentication is the first line of defense in information security. In authentication systems, response time and security are the most important factors. An optical authentication technology based on compressive ghost imaging with QR codes is proposed in this paper. The scheme can be authenticated with a small number of samples. Therefore, the response time of the algorithm is short. At the same time, the algorithm can resist certain noise attacks, so it offers good security.

In response to existing compressed sensing ghost imaging (CSGI) schemes, an innovative Bayesian compressed sensing ghost imaging with better anti-noise performance is proposed, by using the sparse representation of K-Singular Value Decomposition (KSVD) and a 3Level (3L)-hierarchical variational message passing (VMP) algorithm. Simulation and experimental results confirm that, this innovative method overcomes the limitations of presetting specific parameters (sparsity, noise level, etc.), and also demonstrates superior performance in terms of reconstruction accuracy and imaging quality, especially for highly complex objects, where it effectively achieves accurate imaging under varying levels of noise at a low sampling rate (below 12.2%). In addition, compared to existing Bayesian compressive sensing ghost imaging (BCSGI), our algorithm moderately reduces time consumption while ensuring high precision. Our results may provide potential applications of CSGI in the field of biomedical imaging.

The choice of sampling rate is a critical preprocessing step in event-related potential (ERP) research, yet its impact on different analytic approaches remains underexplored. In this study, we systematically evaluated how downsampling affects data quality measured via Standardized Measurement Error (SME), conventional univariate ERP metrics (mean amplitude, peak amplitude, peak latency, and 50% area latency), and multivariate pattern analysis (MVPA; decoding). We analyzed seven commonly studied ERP components: P3, N400, N170, N2pc, mismatch negativity, error-related negativity, and lateralized readiness potential collected from neurotypical young adults. Results showed that both amplitude- and latency-based scores were significantly affected by sampling rate changes, particularly for 50% area latency that presented increased SME (reduced data quality) at lower sampling rates. Amplitude-based measures and their corresponding effect sizes were more influenced by sampling rate variations, whereas latency-based measures were comparatively stable across different temporal resolutions. In contrast, multivariate decoding performance remained highly robust, with decoding accuracy and effect sizes showing minimal variation even at the lowest sampling rate (e.g., 64 Hz). Overall, these findings suggest that caution should be exercised when employing lower sampling rates for data quality and conventional univariate ERP analyses. Nevertheless, lower sampling rates (e.g., 64 Hz) may still be appropriate for decoding analyses, particularly in studies where precise temporal resolution is not critical. For researchers analyzing ERP data with similar components, noise levels, and participant populations as in this study, following these recommendations should yield robust statistical power.

Equipment requirement, quality of reconstructed image, and reconstruction time are important factors in the realization of thermal-light ghost imaging system. In this paper, we propose a new ghost imaging scheme with multiple speckle patterns, named multiple speckle patterns differential compressive ghost imaging scheme. In this scheme, the high temporal resolution requirements for detectors is reduced by continuously detecting multiple independent speckle patterns. We eliminate the background and other noises in ghost imaging system by using differential ghost imaging method. And the reconstruction time is effectively reduced simultaneously to improve the reconstructed image quality by introducing the compressive sensing techniques. Numerical results show that for the two-level grayscale “N” image, the mean square error, in using the proposed scheme with 8000 measurements, is reduced by 96.9%, and the peak signal to noise ratio has improved by 15.1 dB, in comparison with those using the original multiple speckle patterns ghost imaging scheme with 35000 measurements. For the eight-level grayscale “Pepper” image, the peak signal to noise ratio is enhanced by 11.4 dB. The proposed scheme also can decrease the requirements of detection equipment to improve the image quality, and reduce the reconstruction time. Therefore, it may have a broad application prospect.

Cerebrovascular reactivity, cerebral autoregulation (CA), and oxygen delivery can be measured continuously and in a non-invasive fashion using cerebral near-infrared spectroscopy (NIRS). Although the literature is limited surrounding the difference between signals acquired and derived from low (<100 Hz) and high sampling rates (≥100 Hz). As part of a prospective observational study, we preliminarily explored and assessed the difference in the information provided by two NIRS systems using regional cerebral oxygen saturation and cerebral oximetry index signals at low and high sampling rates. The raw data in two frequencies (down-sampled to 1 Hz using the mean and up-sampled to 250 Hz) were decimated to focus on slow-wave vasogenic fluctuations associated with CA. Then, the data were analyzed using various statistical methods such as the absolute signal difference, Pearson correlation, Bland–Altman agreement, Cross-correlation function, optimal time-series autocorrelative structure, time-series impulse response function, and Granger causality relationships. The results of the various statistical analyses indicated that the signals obtained using high-frequency NIRS were different from signals obtained from low-frequency NIRS of the same cerebral region. Hence, high-frequency NIRS systems may possibly contain better signal features compared to NIRS systems with low sampling rates, but further work is required to assess high-frequency NIRS in other healthy and cranial trauma populations.

To get a better understanding of people's mobility, cellular network signalling data including location information, is a promising large-scale data source. In order to estimate travel demand and infrastructure usage from the data, it is necessary to identify the trips users make. We present two trip extraction methods and compare their performance using a small dataset collected in Sweden. The trips extracted are compared with GPS tracks collected on the same mobiles. Despite the much lower location sampling rate in the cellular network signalling data, we are able to detect most of the trips found from GPS data. This is promising, given the relative simplicity of the algorithms. However, further investigation is necessary using a larger dataset and more types of algorithms. By applying the same methods to a second dataset for Senegal with much lower sampling rate than the Sweden dataset, we show that the choice of the trip extraction method tends to be even more important when the sampling rate is low.