Computational Ghost Imaging Research Articles

Computational temporal ghost imaging based on complementary modulation

Abstract We report an experimental demonstration of temporal ghost imaging in which a digital micromirror device (DMD) and +1/−1 binary modulation have been combined to give an accurate reconstruction of a nonperiodic time object. Compared to the 0/1 modulation, the reconstruction signal can be improved greatly by +1/−1 binary modulation even with half of the measurements. Experimental results show that 0/1 binary temporal objects up to 4 kHz and sinusoidal time objects up to 1 kHz can be reconstructed by this method. The influences of modulation speed and array detector gray levels are also discussed.

Computational ghost imaging using the dilated ghost network

Computational ghost imaging has poor imaging quality at low sampling rates. To solve this issue, a dilated ghost network is proposed to improve the quality of noisy images reconstructed by computational ghost imaging in this paper. The network builds upon the traditional GhostNet framework, which minimizes computational costs while maintaining stable performance. By incorporating dilated convolutions, the network expands the receptive field without adding additional parameters. However, the use of dilated convolutions can cause a gridding problem, which degrades the imaging quality. To solve this problem, a sawtooth wave-like structure is introduced into the network. Simulation experiments, combined with metrics such as SSIM, PSNR, and RMSE, validate both the existence of the gridding problem and the effectiveness of the sawtooth wave-like structure in overcoming it. The experimental results indicate that the proposed method significantly outperforms traditional and compressive-sensing-based CGI methods.

20 µm resolution multipixel ghost imaging with high-energy x-rays

Hard x-ray imaging is indispensable across diverse fields owing to its high penetrability. However, the resolution of traditional x-ray imaging modalities, such as computed tomography (CT) systems, is constrained by factors including beam properties, the limitations of optical components, and detection resolution. As a result, the typical resolution in commercial imaging systems that provide full-field imaging is limited to a few hundred microns, and scanning CT systems are too slow for many applications. This study advances high-photon-energy imaging by extending the concept of computational ghost imaging to multipixel ghost imaging with x-rays. We demonstrate a remarkable resolution of approximately 20 µm for an image spanning 0.9 by 1 cm2, comprised of 400,000 pixels and involving only 1000 realizations. Furthermore, we present a high-resolution CT reconstruction using our method, revealing enhanced visibility and resolution. Our achievement is facilitated by an innovative x-ray lithography technique and the computed tiling of images captured by each detector pixel. Importantly, this method maintains reasonable timeframes and can be scaled up for larger images without sacrificing the short measurement time, thereby opening intriguing possibilities for noninvasive high-resolution imaging of small features that are invisible with the present modalities.

Increasing the Resolution of Transmission Electron Microscopy by Computational Ghost Imaging.

By means of numerical simulations, we demonstrate the innovative use of computational ghost imaging in transmission electron microscopy to retrieve images with a resolution that overcomes the limitations imposed by coherent aberrations. The method requires measuring the intensity on a single pixel detector with a series of structured illuminations. The success of the technique is improved if the probes are made to resemble the sample and the patterns cover the area of interest evenly. By using a simple 8 electrode device as a specific example, a twofold increase in resolution beyond the aberration limit is demonstrated to be possible under realistic experimental conditions.

Multi‐image encryption and authentication using computational ghost imaging and singular value decomposition

AbstractThe non‐local imaging characteristics of computational ghost imaging (CGI) make it widely used in the field of information security. However, the information processing method based on traditional CGI has some problems in the process of multi‐image encryption, such as low efficiency and unsatisfactory reconstruction quality. Therefore, this article proposes to use wavelet transform and singular value decomposition technology to help computational ghost imaging to efficiently complete the coding and encryption of multiple images, and use alternating direction multiplier method (ADMM) in the process of image restoration to carry out high‐quality reconstruction of the original encrypted image. The results show that the scheme has good reconstruction effect, strong security and robustness even at a low sampling rate. It provides some reference for digital watermarking technology and cryptography.

Optical image authentication and encryption scheme with computational ghost imaging

Most of the previous image encryption schemes overlook certifying the source of the encrypted images, thus the receiver may be misled by messages coming from unauthorized users or wrong sources. To simultaneously accomplish image encryption and source authentication of the encrypted images, an optical image authentication and encryption scheme is proposed by integrating computational ghost imaging (CGI) with quick response (QR) code. Firstly, the plaintext image is converted into a QR code and encrypted by a scrambling-diffusion structure. Then, the authentication image is enciphered by a CGI system and a diffusion structure. The intermediate resulting images are marked and embedded into the random matrices to withstand the differential attack. Moreover, the source of the encrypted images can be identified by the reconstructed authentication image and the nonlinear correlation coefficient (NCC) result. The authentication is considered successful if a sharp peak appears in the center of the NCC result. Numerical simulations show that the proposed scheme can not only withstand different attacks, but also avoid being misled by misinformation during the decryption process.

Computational ghost imaging enhanced by degradation models for under-sampling.

Computational ghost imaging (CGI) allows two-dimensional (2D) imaging by using spatial light modulators and bucket detectors. However, most CGI methods attempt to obtain 2D images through measurements with a single sampling ratio. Here, we propose a CGI method enhanced by degradation models for under-sampling, which can be reflected by results from measurements with different sampling ratios. We utilize results from low-sampling-ratio measurements and normal-sampling-ratio measurements to train the neural network for the degradation model, which is fitted through self-supervised learning. We obtain final results by importing normal-sampling-ratio results into the neural network with optimal parameters. We experimentally demonstrate improved results from the CGI method using degradation models for under-sampling. Our proposed method would promote the development of CGI in many applications.

Simulation‐Training‐Based Deep Learning Approach to Microscopic Ghost Imaging

Herein, deep learning‐ghost imaging (DLGI) based on a digital micromirror device is realized to avoid the difficulties of a charge‐coupled device (CCD) scientific camera being unable to obtain the sample images in extremely weak illumination conditions and to solve the problem of the inverse relationship between imaging quality and imaging time in practical applications. Deep learning for computational ghost imaging typically requires the collection of a large set of labeled experimental data to train a neural network. Herein, we demonstrate that a practically usable neural network can be prepared based on the simulation results. The acquisition results of the CCD scientific camera and the simulation results with low sampling are used as the training set (1000 observations) and we can complete the data acquisition process within one hour. The results show that the proposed DLGI method can be used to significantly improve the quality of the reconstructed images when the sampling rate is 60%. This method also reduces the imaging time and the memory usage, while simultaneously improving the imaging quality. The imaging results of the proposed DLGI method have great significance for application in clinical diagnosis.

Multi-Wavelength Computational Ghost Imaging Based on Feature Dimensionality Reduction

Multi-Wavelength Computational Ghost Imaging Based on Feature Dimensionality Reduction

Computational underwater ghost imaging based on scattering-and-absorption degradation.

In underwater computational ghost imaging, the presence of scattering and absorption introduces significant degradation, leading to blurring and distortion of illuminating patterns. This work proposes an anti-degradation underwater computational ghost imaging (AUGI) method based on the physical degradation model of underwater forward degradation caused by scattering and absorption. Through AUGI, we can enhance the quality of a reconstructed image by about 10% compared to differential ghost imaging (DGI) as measured by peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), as a result of simulations. We experimentally demonstrate the superior effectiveness of this method in the artificial submarine environment. Additionally, benefitting from its simplicity, this method is expected to be applied across a wide range of underwater ghost imaging applications.

Detection Model and Correction Method for Quadrant Detector-Based Computational Ghost Imaging System

Detection Model and Correction Method for Quadrant Detector-Based Computational Ghost Imaging System

Multi-channel computational ghost imaging based on multi-scale speckle optimization

A multi-channel computational ghost imaging (GI) method based on multi-scale speckle optimization is proposed. We not only reduce imaging time and enhance imaging quality but also reduce interference among different channels. Using one bucket detector to receive total light intensity, the color speckle is formed by combining components obtained through the singular value decomposition of three self-designed multi-scale measurement matrices. Simulation and experimental results demonstrate that our designed method contributes to reducing imaging time and enhancing imaging quality, achieving improved visual quality even at low sampling rates. This approach enhances GI flexibility and holds potential for diverse applications, including target recognition and biomedical imaging.

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.

A crosstalk-free multiple-image encryption scheme based on computational ghost imaging with binarized detection

Crosstalk noise is a main problem limiting the performance of multiple-image encryption (MIE) scheme. In this work, we proposed a crosstalk-free MIE scheme based on computational ghost imaging (CGI) with binarized detection. In the encryption process, the plaintext images are encrypted into intensity sequences by the CGI system and quantified into two levels to obtain binary ciphertext sequences, which does not cause severe degradation in decrypted image quality compared to traditional CGI. Then, for the binary ciphertext sequences, we can combine them into a decimal grayscale ciphertext. To enhance security, a pixel bit layer scrambling (PBLS) algorithm is designed to scramble the grayscale ciphertext to obtain the final ciphertext. In the decryption process, anyone of the plaintext images can be decrypted without being affected by other plaintext images after performing inverse PBLS algorithm on the ciphertext and extracting the binary ciphertext sequence. The effectiveness, robustness, encryption capacity and security of the proposed scheme are demonstrated by numerical simulations and theoretical analysis.

Mid-infrared computational temporal ghost imaging

Ghost imaging in the time domain allows for reconstructing fast temporal objects using a slow photodetector. The technique involves correlating random or pre-programmed probing temporal intensity patterns with the integrated signal measured after modulation by the temporal object. However, the implementation of temporal ghost imaging necessitates ultrafast detectors or modulators for measuring or pre-programming the probing intensity patterns, which are not available in all spectral regions especially in the mid-infrared range. Here, we demonstrate a frequency downconversion temporal ghost imaging scheme that enables to extend the operation regime to arbitrary wavelengths regions where fast modulators and detectors are not available. The approach modulates a signal with temporal intensity patterns in the near-infrared and transfers the patterns to an idler via difference-frequency generation in a nonlinear crystal at a wavelength where the temporal object can be retrieved. As a proof-of-concept, we demonstrate computational temporal ghost imaging in the mid-infrared with operating wavelength that can be tuned from 3.2 to 4.3 μm. The scheme is flexible and can be extended to other regimes. Our results introduce new possibilities for scan-free pump-probe imaging and the study of ultrafast dynamics in spectral regions where ultrafast modulation or detection is challenging such as the mid-infrared and THz regions.

Anti-noise computational ghost imaging based on wavelet threshold denoising

Noise can inevitably affect imaging quality of optical imaging system. In this paper, a method of computational ghost imaging (CGI) with better anti-noise performance is proposed by using wavelet threshold denoising (WTD). The simulation and experimental results show that this method exhibits better robustness against both the light source noise and the path noise. To explain this phenomenon, the decline rate of the correlation coefficients between speckle patterns (CCSP) under different noise levels are analyzed. As a post-processing method, our method is simple to implement and is applicable to both random speckle CGI and orthogonal modulated CGI, potentially broadening application prospects in practical applications of ghost imaging.

Single-pixel imaging based on self-supervised conditional mask classifier-free guidance

Reconstructing high-quality images at a low measurement rate is a pivotal objective of Single-Pixel Imaging (SPI). Currently, deep learning methods achieve this by optimizing the loss between the target image and the original image, thereby constraining the potential of low measurement values. We employ conditional probability to ameliorate this, introducing the classifier-free guidance model (CFG) for enhanced reconstruction. We propose a self-supervised conditional masked classifier-free guidance (SCM-CFG) for single-pixel reconstruction. At a 10% measurement rate, SCM-CFG efficiently completed the training task, achieving an average peak signal-to-noise ratio (PSNR) of 26.17 dB on the MNIST dataset. This surpasses other methods of photon imaging and computational ghost imaging. It demonstrates remarkable generalization performance. Moreover, thanks to the outstanding design of the conditional mask in this paper, it can significantly enhance the accuracy of reconstructed images through overlay. SCM-CFG achieved a notable improvement of an average of 7.3 dB in overlay processing, in contrast to only a 1 dB improvement in computational ghost imaging. Subsequent physical experiments validated the effectiveness of SCM-CFG.

Improving the reliability of deep learning computational ghost imaging with prediction uncertainty based on neighborhood feature maps

Defect inspection is required in various fields, and many researchers have attempted deep-learning algorithms for inspections. Deep-learning algorithms have advantages in terms of accuracy and measurement time; however, the reliability of deep-learning outputs is problematic in precision measurements. This study demonstrates that iterative estimation using neighboring feature maps can evaluate the uncertainty of the outputs and shows that unconfident error predictions have higher uncertainties. In ghost imaging using deep learning, the experimental results show that removing outputs with higher uncertainties improves the accuracy by approximately 15.7%.

Multi-user visible light communication based on computational temporal ghost imaging and code division multiple access with wide field of view and encryption

In this paper, we propose a wide field of view optical encrypted communication based on computational temporal ghost imaging (CTGI) and code division multiple access (CDMA) with a violet series-biased micro-light-emitting diode (micro-LED) in visible light communication (VLC) system. Using CDMA to achieve multi-user communication, and CTGI to achieve secure transmission and improve signal quality, it is demonstrated that the algorithm ensures the transmission of LED under large field of view (FOV), with error-free communication at a wide angle of 120°. In terms of anti-interference encryption, correct demodulation can be achieved under 45% occlusion attack or 80% Gaussian noise attack, with a total of 6.16×10216 possibilities to ensure the security of user's information, providing an approach for the practical application of VLC system.

Adaptive Ghost Imaging Based on 2D-Haar Wavelets

To improve the imaging speed of ghost imaging and ensure the accuracy of the images, an adaptive ghost imaging scheme based on 2D-Haar wavelets has been proposed. This scheme is capable of significantly retaining image information even under under-sampling conditions. By comparing the differences in light intensity distribution and sampling characteristics between Hadamard and 2D-Haar wavelet illumination patterns, we discovered that the lateral and longitudinal information detected by the high-frequency 2D-Haar wavelet measurement basis could be used to predictively adjust the diagonal measurement basis, thereby reducing the number of measurements required. Simulation and experimental results indicate that this scheme can still achieve high-quality imaging results with about a 25% reduction in the number of measurements. This approach provides a new perspective for enhancing the efficiency of computational ghost imaging.