Multiple speckle patterns differential compressive ghost imaging

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.

We introduce sinusoidal ghost imaging (SGI), which uses 2D orthogonal sinusoidal patterns instead of random patterns in "computational ghost imaging" (CGI). Simulations and experiments are performed. In comparison with the"differential ghost imaging" algorithm that was used to improve the SNR of ghost imaging, results of SGI show about 3 orders of magnitude higher SNR, which can be reconstructed even with a much smaller number of patterns. More importantly, based on the results, SGI provides the great opportunity to generate innate processed images by predefined selection of patterns. This can speed up detection process considerably and paves the way for real applications.

We demonstrate a novel ghost imaging (GI) scheme based on one single-photon-counting detector with subsequent photon statistics analysis. The key idea is that instead of measuring correlations between the object and reference beams such as in standard GI schemes, the light of the two beams is superimposed. The photon statistics analysis of this mixed light allows us to determine the photon number distribution as well as to calculate the central second-order correlation coefficient. The image information is obtained as a function of the spatial resolution of the reference beam. The performance of this photon-statistics-based GI system with one single detector (PS-GI) is investigated in terms of visibility and resolution. Finally, the knowledge of the complete photon statistics allows easy access to higher correlation coefficients such that we are able to perform here third- and fourth-order GI. The PS-GI concept can be seen as a complement to already existing GI technologies thus enabling a broader dissemination of GI as a superior metrology technique, paving the road for new applications in particular with advanced photon counting detectors.

Digit classification of ghost imaging based on similarity measures

In the ghost imaging system based on the multidimensional vector matrix Walsh transform, the Walsh speckle pattern is used to continuously sample the moving target object, so there is a certain correlation and complementary detail information between multiple frames in the ghost imaging video. In our previous research, we broke through the inherent limitations of the digital micromirror device refresh rate on ghost imaging systems, allowing us to reconstruct more detailed frames. Therefore, by utilizing the correlation between these detailed frames in the time and space domains, we can improve the comfort of ghost imaging videos from the perspective of improving the single-frame quality of ghost imaging videos. To further improve multi-frame quality by utilizing more detailed frames in ghost imaging videos, this paper proposes a ghost imaging video algorithm based on the multidimensional vector matrix Walsh transform of bidirectional N-aligned fusion frames. Combining deep learning with computational ghost imaging, utilizing a bidirectional N-alignment algorithm and a deep learning neural network framework for target frame integration. The ghost imaging videos obtained from previous research can be improved in terms of noise, motion blur, and single-frame detail richness to enhance the imaging quality of the target frame. This paper constructs an encoding module and a corresponding feature fusion module suitable for multidimensional vector Walsh transform ghost imaging from the perspective of network width and feature multi-branch extraction based on GoogleNet Inception V3. A loss function suitable for four-dimensional vector matrix Walsh transform ghost imaging has been defined, which can better eliminate the noise and distortion caused by Walsh speckle sampling. After comparing the experimental results of moving objects, the results show that the algorithm proposed in this paper has significantly improved structural similarity, blur index, noise index, and other aspects compared to existing ghost imaging video optimization methods. The similarity angle of the NRSS structure has increased by 18.58% compared to the original reconstructed image, the blur parameter has increased by 31.9%, and the noise parameter has risen by 9.22%.

In practical thermal light ghost imaging, the nonuniform intensity distribution of the thermal light applied causes distortion of the retrieved image. We propose uniformly weighted arithmetics, including uniformly weighted ghost imaging and uniformly weighted differential ghost imaging (UWDGI), to improve the imaging quality of ghost imaging with nonuniform thermal light fields. Two kinds of nonuniform thermal light fields are discussed, including planar Gaussian distributed thermal light and near-field three-dimensional thermal light. The retrieved image using a uniformly weighted arithmetic has a lower distortion than that using the corresponding traditional ghost imaging arithmetic. In addition, the best imaging quality is achieved with the UWDGI arithmetic.

Ghost imaging (GI) through dynamic and complex scattering media remains challenging. The existence of dynamic scattering gives rise to a failure of GI schemes. Here, we report a deep learning-enhanced GI scheme with supervised corrections (SCGI) of dynamic scaling factors to realize high-resolution ghost reconstruction through dynamic and complex scattering media. The SCGI scheme is developed to approximate the variation of dynamic scaling factors in an optical channel and correct the recorded light intensities with a Gaussian prior. An untrained neural network powered by regularization by denoising for the SCGI scheme (SCGI-URED) is developed to further recover high-visibility ghost images. Experimental results demonstrate that high-resolution and high-visibility GI can be realized in dynamic and complex scattering media. The proposed method provides a reliable tool for implementing high-resolution and high-visibility GI through dynamic and complex scattering media and could give an impetus to developing dynamic scattering imaging in real-world scenarios.

The optical transfer function (OTF), which correspond to the frequency domain, is of fundamental importance in characterizing imaging ability in optical imaging systems. By using the Collins formula and the optical transfer matrix theory, a simple and unified analytical expression of OTF is presented in any ghost imaging (GI) system. By comparing the spectrum distribution of OTF between different GI systems, one can predict which imaging system can provide better imaging quality when the same imaging object is used. Theoretical and experimental results are also implemented to verify our results.

In ghost imaging, an illumination light is split into test and reference beams which pass through two different optical systems respectively and an image is constructed with the second-order correlation between the two light beams. Since both light beams are diffracted when passing through the optical systems, the spatial resolution of ghost imaging is in general lower than that of a corresponding conventional imaging system. When Gaussian-shaped light spots are used to illuminate an object, randomly scanning across the object plane, in the ghost imaging scheme, we show that by localizing central positions of the spots of the reference light beam, the resolution can be increased by a factor of same as that of the corresponding conventional imaging system. We also find that the resolution can be further enhanced by setting an appropriate threshold to the bucket measurement of ghost imaging.

Ghost imaging (GI) retrieves an image from the correlation between a sequence of illumination patterns on the object and their corresponding bucket detections. Traditionally, GI requires the precise information of the illumination patterns, which raises technology barriers on building a high-speed illumination source, limiting the scope of its application. In this study, we propose a high-speed GI system, which implements a self-correlation with a purely optical operation without determining illumination patterns. The light source is an optical phased array (OPA), built of a set of waveguide-type electro-optic phase modulators. The OPA is driven to randomly change the phases in every 200 ns, generating speckle patterns at a rate of 5 MHz. Although the speckle patterns are not predictable or post-determinable, with the help of the naked-eye GI scheme, the system in real time optically generates the images of the object at a frame rate of more than 1 kHz, which can be directly observed by eyes or using a camera. This method avoids acquiring the information of the illumination, thus providing a simple and easy way to realize high-speed GI. It also inspires a different way of applying OPAs to high-speed imaging.

Ghost imaging is an indirect system that allows the imaging of an object without directly seeing the object. The speckle pattern that contains the information about light and objects has increasingly become a popular topic in pseudothermal light ghost imaging. However, existing research still has encountered problems of poor imaging quality and slow sampling speeds. We propose a ghost imaging method based on N-order speckle patterns to recover the object (NSGI). The N-order speckle patterns combine N independent laser speckles individually produced by passing an expanded and collimated He–Ne laser through a digital micromirror device (DMD). The sampling frequency can be improved by controlling the trigger signals of different DMDs. The results of the simulation and experiment have verified that our method can increase sampling speed and reconstruction accuracy. In addition, NSGI can be applied to more applications by designing multiple independent speckles with different properties.

Ghost imaging has recently been successfully achieved in the X-ray regime. Due to the penetrating power of X-rays this immediately opens up the possibility of ghost-tomography. No research into this topic currently exists in the literature. Here, we present adaptations of conventional X-ray tomography techniques to this new ghost-imaging scheme. Several numerical implementations for tomography through X-ray ghost-imaging are considered. Specific attention is paid to schemes for reducing the noise-like artefacts of the resulting tomographic reconstruction, issues related to dose fractionation, and considerations regarding the ensemble of illuminating masks used for ghost-imaging. Each theme is explored through a series of numerical simulations, and several suggestions offered for practical realisations of ghost-tomography.

Edge detection is an essential procedure in biomedical image processing. Note that several edge detection schemes based on ghost imaging (GI) have been proposed, while a practical application environment is not considered. In this paper, we first investigate the edge detection of target objects inside biological tissue in a GI system. The performances of four edge detection operators are compared in GI through biological tissue when refractive index turbulence is considered. The numerical results demonstrate that the Standard operator performs better in edge extraction regardless of the strength of refractive index fluctuations. Our results may provide potential applications of GI in the field of biomedical imaging.

Sunlight background noise significantly hinders the operation of ghost imaging systems, posing a considerable challenge for target imaging under daytime sunlight conditions. This paper introduces a method to eliminate sunlight background noise using a Faraday anomalous dispersion optical filter (FADOF). A ghost imaging system based on FADOF is constructed, and experiments are conducted under strong background noise conditions. The system operates outdoors in direct sunlight, utilizing sunlight introduction system to introduce light into the detection path, directly reaching the detection end of the system. In this real sunlight noise condition, the ghost imaging system using FADOF filtering achieved nearly continuous and stable imaging throughout three summer days, contrasting with a system using a 10 nm filter that only functions properly after sunset. The research findings indicate that FADOF effectively enhances the ghost imaging system's resistance to background light noise, enabling continuous operation under conditions of strong background noise throughout the day.

Influence of detector response speed on the contrast-to-noise ratio of reflective ghost imaging