Ghost Imaging Algorithm Research Articles

Optical watermark scheme based on singular value decomposition ghost imaging and particle swarm optimization algorithm

An optical image watermarking algorithm, based on singular value decomposition (SVD) ghost imaging and multiple transforms, is designed. The watermark image is first encrypted by applying an SVD ghost imaging system, then the encrypted watermark is embedded into the cover image with the help of multiple transforms, including lifting wavelet transform (LWT), discrete cosine transform (DCT), discrete fractional angular transform (DFAT) and SVD. Four sub-band images are produced from the host image by LWT and DCT. The improved DFAT, whose scaling factors and parameter are optimized by particle swarm optimization algorithm, is operated in the new matrix. Afterwards, SVD is executed in the two-part image and the encrypted watermark is embedded in the host image by mutual operation of different matrices. Simulation results validate that the proposed watermark scheme is superior in the aspects of security, robustness and imperceptibility.

Hybrid cryptosystem based on plaintext related computational ghost imaging encryption and elliptic curve algorithm

A hybrid cryptosystem based on plaintext-related ghost imaging and elliptic curve public-key cryptographic algorithm (ECC) is proposed in this paper. Thereinto, the ECC algorithm is used to protect and distribute session keys and the optical encryption system with session keys is used to encrypt and decrypt images. Thousands of phase-only mask keys can be replaced by parameter keys in this method, which successfully reduces the pressure of key storage. With dynamic updating session keys, the attacks are effectively prevented and the security resilience is enhanced. In addition, the utilization of an ECC cryptosystem solves the inevitable problem of key management and dispatch in conventional ghost imaging encryption system. The feasibility, security and robustness of the method are further analyzed through computer simulations.

Instant single-pixel imaging: on-chip real-time implementation based on the instant ghost imaging algorithm

Single-pixel imaging (SPI) uses a single-pixel detector to create an image of an object. SPI relies on a computer to construct an image, thus increasing both the size and cost of SPI and limiting its application. We developed instant single-pixel imaging (ISPI), an on-chip SPI system that implements real-time imaging at a rate of 25 fps. ISPI uses the instant ghost imaging algorithm we proposed, which leverages signal differences for image creation. It does not require a computer, which greatly reduces its both cost and size. The reconstruct time of ISPI for image creation is almost zero because little processing is required after signal detection. ISPI paves the way for the practical application of SPI.

Instant ghost imaging: improving robustness for ghost imaging subject to optical background noise

Ghost imaging (GI) is an imaging technique that uses the second-order correlation between two light beams to obtain the image of an object. However, standard GI is affected by optical background noise, which reduces its practical use. We investigated the robustness of an instant ghost imaging (IGI) algorithm against optical background noise and compare it with the conventional GI algorithm. Our results show that IGI is extremely resistant to spatiotemporally varying optical background noise that can change over a large range. When the noise is large in relation to the signal, IGI will still perform well in conditions that prevent the conventional GI algorithm from generating an image because IGI uses signal differences for imaging. Signal differences are intrinsically resistant to common noise modes, so the IGI algorithm is strongly robust against noise. This research is of great significance for the practical application of GI.

Instant ghost imaging: algorithm and on-chip implementation.

Ghost imaging (GI) is an imaging technique that uses the correlation between two light beams to reconstruct the image of an object. Conventional GI algorithms require large memory space to store the measured data and perform complicated offline calculations, limiting practical applications of GI. Here we develop an instant ghost imaging (IGI) technique with a differential algorithm and an implemented high-speed on-chip IGI hardware system. This algorithm uses the signal between consecutive temporal measurements to reduce the memory requirements without degradation of image quality compared with conventional GI algorithms. The on-chip IGI system can immediately reconstruct the image once the measurement finishes; there is no need to rely on post-processing or offline reconstruction. This system can be developed into a realtime imaging system. These features make IGI a faster, cheaper, and more compact alternative to a conventional GI system and make it viable for practical applications of GI.

Research on the information transmission based on linear block code and temporal ghost imaging algorithm

In recent years, ghost imaging technology, which uses a barrel detector without spatial resolution to detect and reconstruct object information separately, has become an important research object in the field of quantum optics. In this paper, based on the concrete analysis of ghost imaging algorithm on the basis of information coding and transmission, the information coding transmission is completed by combining the error-checking and error-correcting function of linear block code coding and the precise transmission function of temporal ghost imaging. This paper studied and analyzed the information transmission mechanism based on temporal ghost imaging from three aspects: feasibility, robustness and bit error rate. Based on triangular wave signal information, the simulation and experimental analysis shows that the information coding transmission mechanism improves the difficulty of decoding the code by the attacker, greatly enhances the ability of anti-interference and anti-interception, reduces the bit error rate, realizes the accurate information coding transmission, and solves the problem of the current imaging algorithm which is not feasible and the degree of reduction is not high.

Grayscale and color ghost-imaging of moving objects by memory-enabled, memoryless and compressive sensing algorithms

Grayscale and color computational ghost imaging of a moving object is investigated. Three different ghost imaging algorithms, memory-enabled, memoryless and compressive sensing, are employed and compared. A speckle resizing method is also applied to enhance the features of the obtained ghost images. The results indicate that the compressive sensing algorithm provides better SNR and visibility values, while the memory-enabled algorithm offers higher imaging speeds. It is shown that for shot numbers ranging from 150–400, SNR values as high as 1.5, visibilities around 0.95 and maximum target speeds about 0.65 pixel/s are affordable by proper selection of the applied ghost imaging algorithm. Also, a method for color ghost imaging of moving objects through RGB illumination is introduced. Furthermore, results for experimental computational ghost imaging of a moving object along with the maximum practicable speed are included.

Study on image transmission mechanism of ghost imaging based on joint source and channel coding

In this paper, through the detailed analysis of ghost imaging algorithm and joint source–channel coding transmission, it not only combines the efficient compression function of source code, and the error detection and error correction function of channel code, but also combines the accurate transmission function of ghost imaging, which finally completes the coding transmission of the image. In this paper, the image transmission mechanism of ghost imaging based on joint source–channel coding is studied, and the feasibility, bit error rate and robustness are analyzed respectively. The transmission mechanism of the image coding can improve the compressibility of the coded image and the difficulty of being deciphered by the attacker, reduce the bit error rate in the process of the transmission of the image coding, enhance the ability of anti-interference and anti-interception, realize the accurate image coding transmission, and it also solves the problems such as lack of feasibility, low reduction degree and poor imaging quality of current imaging algorithms.

Compressed-Sensing-based Gradient Reconstruction for Ghost Imaging

In this paper, we propose a compression sensing ghost imaging algorithm to reduce the computation time with high image quality via compression sensing based on the total variation reconstruction. A small amount of measurements can be used for shortening the sampling time. The total variation is used as criteria during the search process. It makes the ghost image achieving a high image reconstruction quality. The simulation results demonstrate that the proposed method can enhance imaging quality with the reduced computation time.

Image quality enhancement in low-light-level ghost imaging using modified compressive sensing method

Detector noise has a significantly negative impact on ghost imaging at low light levels, especially for existing recovery algorithm. Based on the characteristics of the additive detector noise, a method named modified compressive sensing ghost imaging is proposed to reduce the background imposed by the randomly distributed detector noise at signal path. Experimental results show that, with an appropriate choice of threshold value, modified compressive sensing ghost imaging algorithm can dramatically enhance the contrast-to-noise ratio of the object reconstruction significantly compared with traditional ghost imaging and compressive sensing ghost imaging methods. The relationship between the contrast-to-noise ratio of the reconstruction image and the intensity ratio (namely, the average signal intensity to average noise intensity ratio) for the three reconstruction algorithms are also discussed. This noise suppression imaging technique will have great applications in remote-sensing and security areas.

Negative influence of detector noise on ghost imaging based on the photon counting technique at low light levels

The influence of detector noise on ghost imaging (GI) is investigated at low light levels. Based on the characteristics of the additive detector noise, we establish the analytical model and display the ghost images through numerical and experimental demonstrations. It is shown that the contrast-to-noise ratio and visibility of reconstructed images are sharply affected by the detector noise. Following the increase of the ratio of average signal intensity to the average noise, the quality of reconstructions is enhanced. To reduce the measurement numbers and, thus, shorten the consuming time without sacrificing the imaging quality, we propose a sorting technique in the traditional GI algorithm for a high quality image reconstruction. The results demonstrated here will be favorable to the applications of low-light-level imaging.

Adaptive differential correspondence imaging based on sorting technique

We develop an adaptive differential correspondence imaging (CI) method using a sorting technique. Different from the conventional CI schemes, the bucket detector signals (BDS) are first processed by a differential technique, and then sorted in a descending (or ascending) order. Subsequently, according to the front and last several frames of the sorted BDS, the positive and negative subsets (PNS) are created by selecting the relative frames from the reference detector signals. Finally, the object image is recovered from the PNS. Besides, an adaptive method based on two-step iteration is designed to select the optimum number of frames. To verify the proposed method, a single-detector computational ghost imaging (GI) setup is constructed. We experimentally and numerically compare the performance of the proposed method with different GI algorithms. The results show that our method can improve the reconstruction quality and reduce the computation cost by using fewer measurement data.

High-quality correspondence imaging based on sorting and compressive sensing technique

We propose a high-quality imaging method based on correspondence imaging (CI) using a sorting and compressive sensing (CS) technique. Unlike the traditional CI, the positive and negative (PN) subsets are created by a sorting method, and the image of an object is then recovered from the PN subsets using a CS technique. We compare the performance of the proposed method with different ghost imaging (GI) algorithms using the data from a single-detector computational GI system. The results demonstrate that our method enjoys excellent imaging and anti-interference capabilities, and can further reduce the measurement numbers compared with the direct use of CS in GI.

Computational imaging based on time-correlated single-photon-counting technique at low light level

Imaging at low light levels has drawn much attention. In this paper, a method is experimentally demonstrated to realize computational imaging under weak illumination conditions. In our experiment, only one single-photon detector was used to capture the photons. With the time-correlated single-photon-counting technique, photons at a quite low level can be recorded and the time distribution histograms were constructed. The intensity of the light can be estimated from the histograms. The detection model was discussed, and clear images were obtained through a ghost-imaging algorithm. In addition, we propose a modified algorithm for the conventional ghost-imaging method that works more efficiently than the traditional ghost-imaging algorithm. Moreover, this method provides a solution for three-dimensional imaging combining with the time of flight of the photons.

Increasing the range accuracy of three-dimensional ghost imaging ladar using optimum slicing number method**Project supported by the Young Scientist Fund of the National Natural Science Foundation of China (Grant No. 61108072).

The range accuracy of three-dimensional (3D) ghost imaging is derived. Based on the derived range accuracy equation, the relationship between the slicing number and the range accuracy is analyzed and an optimum slicing number (OSN) is determined. According to the OSN, an improved 3D ghost imaging algorithm is proposed to increase the range accuracy. Experimental results indicate that the slicing number can affect the range accuracy significantly and the highest range accuracy can be achieved if the 3D ghost imaging system works with OSN.

Evaluation criterion of thermal light ghost imaging based on the receiver operating characteristic analysis.

The performances of different thermal ghost imaging (GI) algorithms are compared in an experiment of computational GI using a digital micromirror device. Here we present a rather different evaluation criterion named receiver operating characteristic (ROC) analysis that serves as the performance of merit for the quantitative comparison. A ROC curve is created by plotting the true positive rate against the false positive rate at various threshold settings. Both theoretical analysis and experimental results demonstrate that the ROC curve and the area under the curve are better and more intuitive indicators of the performance of the GI, compared with conventional evaluation methods. Additionally, for examining gray-scale objects, the calculation of the volume under the ROC surface is analyzed and serves as a performance metric. Our scheme should attract general interest and open exciting prospects for ROC analysis in thermal GI.

Super-resolution ghost imaging via compressed sensing

Achieving high resolution images is of great importance in ghost imaging. We present a super-resolution image reconstruction algorithm with sparse measurements based on the theory of compressed sensing and the prior knowledge of the point spread function of the ghost imaging system. A computational ghost imaging experimental setup with a digital mirror device is built to verify the effect of this algorithm on increasing the resolution of the ghost imaging system. In addition, we compare the result with that from the traditional ghost imaging algorithm. The experiments show that we can obtain super-resolution images by this algorithm with the sparse measurements. This approach can break through the Rayleigh limit of the imaging system and obtain super-resolution images.

Detailed quality analysis of ideal high-order thermal ghost imaging

The performances of ideal high-order thermal ghost imaging, including conventional high-order ghost imaging, background-subtracted high-order ghost imaging (BSGI), and intensity fluctuation high-order ghost imaging (IFGI), are compared in this paper. The detailed quality analyses of the three high-order ghost imaging algorithms are demonstrated. The signal-to-background ratio and contrast-to-noise ratio of the pattern-normalized high-order ghost imaging with an arbitrary-pixel object are deduced, which are in great agreement with the experimental results. Experimental results indicate that the best performance is achieved by the lowest-order BSGI or the lowest-order IFGI.

Normalized ghost imaging

We present an experimental comparison between different iterative ghost imaging algorithms. Our experimental setup utilizes a spatial light modulator for generating known random light fields to illuminate a partially-transmissive object. We adapt the weighting factor used in the traditional ghost imaging algorithm to account for changes in the efficiency of the generated light field. We show that our normalized weighting algorithm can match the performance of differential ghost imaging.