Ghost imaging (GI) is a novel imaging technique based on the second-order correlation of light fields. Due to limited number of samplings in practice, traditional GI methods often reconstruct objects with unsatisfactory quality. To improve the imaging results, many reconstruction methods have been developed, yet the reconstruction quality is still fundamentally restricted by the modulated light fields. In this paper, we propose to improve the imaging quality of GI by optimizing the light fields, which is realized via matrix optimization for a learned dictionary incorporating the sparsity prior of objects. A closed-form solution of the sampling matrix, which enables successive sampling, is derived. Through simulation and experimental results, it is shown that the proposed scheme leads to better imaging quality compared to the state-of-the-art optimization methods for light fields, especially at a low sampling rate.
We present a framework for computational ghost imaging based on deep learning and customized pink noise speckle patterns. The deep neural network in this work, which can learn the sensing model and enhance image reconstruction quality, is trained merely by simulation. To demonstrate the sub-Nyquist level in our work, the conventional computational ghost imaging results, reconstructed imaging results using white noise and pink noise via deep learning are compared under multiple sampling rates at different noise conditions. We show that the proposed scheme can provide high-quality images with a …
Ghost imaging (GI) is a novel imaging method, which can reconstruct the object information by the light intensity correlation measurements. However, at present, the field of view (FOV) is limited to the illuminating range of the light patterns. To enlarge FOV of GI efficiently, here we proposed the omnidirectional ghost imaging system (OGIS), which can achieve a 360° omnidirectional FOV at one shot only by adding a curved mirror. Moreover, by designing the retina-like annular patterns with log-polar patterns, OGIS can obtain unwrapping-free undistorted panoramic images with uniform resolution,…
We propose a new local-binary ghost imaging by using point-by-point method. This method can compensate the degradation of imaging quality due to the loss of information during binarization process. The numerical and experimental results show that the target details can be reconstructed well by this method when compared with traditional ghost imaging. By comparing the differences of the speckle patterns from different binarization methods, we also give the corresponding explanation. Our results may have the potential applications in areas with high requirements for imaging details, such as targ…
Nowadays the world has entered into the digital age, in which the data analysis and visualization have become more and more important. In analogy to imaging the real object, we demonstrate that the computational ghost imaging can image the digital data to show their characteristics, such as periodicity. Furthermore, our experimental results show that the use of optical imaging methods to analyse data exhibits unique advantages, especially in anti-interference. The data analysis with computational ghost imaging can be well performed against strong noise, random amplitude and phase changes in th…
The unpaired training can be the only option available for fast deep learning-based ghost imaging, where obtaining a high signal-to-noise ratio (SNR) image copy of each low SNR ghost image could be practically time-consuming and challenging. This paper explores the capabilities of deep learning to leverage computational ghost imaging when there is a lack of paired training images. The deep learning approach proposed here enables fast ghost imaging through reconstruction of high SNR images from faint and hastily shot ghost images using a constrained Wasserstein generative adversarial network. I…
Turbulence-free images cannot be produced by conventional computational ghost imaging because calculated light is not affected by the same atmospheric turbulence as real light. In this article, we first addressed this issue by measuring the photon number fluctuation autocorrelation of the signals generated by a conventional computational ghost imaging device. Our results illustrate how conventional computational ghost imaging without structural changes can be used to produce turbulence-free images.
We study the influence rules of the speckle size of light source on ghost imaging, and propose a new type of speckle patterns to improve the quality of ghost imaging. The results show that the image quality will first increase and then decrease with the increase of the speckle size, and there is an optimal speckle size for a specific object. Moreover, by using the random distribution of speckle positions, a new type of displacement speckle patterns is designed, and the imaging quality is better than that of the random speckle patterns. These results are of great significances for finding the b…
There is a consensus that turbulence-free images cannot be obtained by conventional computational ghost imaging (CGI) because the CGI is only a classic simulation, which does not satisfy the conditions of turbulence-free imaging. In this article, we first report a turbulence-immune CGI method based on a multi-scale generative adversarial network (MsGAN). Here, the conventional CGI framework is not changed, but the conventional CGI coincidence measurement algorithm is optimized by an MsGAN. Thus, the satisfactory turbulence-free ghost image can be reconstructed by training the network, and the …
In this paper, we present a method for speckle pattern design using deep learning. The speckle patterns possess unique features after experiencing convolutions in Speckle-Net, our well-designed framework for speckle pattern generation. We then apply our method to the computational ghost imaging system. The standard deep learning-assisted ghost imaging methods use the network to recognize the reconstructed objects or imaging algorithms. In contrast, this innovative application optimizes the illuminating speckle patterns via Speckle-Net with specific sampling ratios. Our method, therefore, outpe…
Ghost imaging can capture 2D images with a point detector instead of an array sensor. It therefore offers a solution to the challenge of building area format sensors in wavebands where such sensors are difficult and expensive to produce and opens up new imaging modalities due to high-performance single-pixel detectors. Traditionally, ghost imaging retrieves the image of an object offline, by correlating measured light intensities and applied illuminating patterns. Here we present a feedback-based approach for online updating of the imaging result that can bypass post-processing, termed self-ev…
The nature of multiple samples to extract correlation information limits the applications of ghost imaging of moving objects. A novel multi-to-one neural network is proposed and the concept of "batch frame" is introduced to improve the serial imaging method. The neural network extracts more correlation information from a small number of samples, thus reducing the sampling ratio of the ghost imaging technique. We combine the correlation characteristics between images to propose a frame merging algorithm, which eliminates the dynamic blur of high-speed moving objects and further improves the rec…
We develop and experimentally demonstrate an imaging method based on the pink noise pattern in the computational ghost imaging (CGI) system, which has a strong ability to photograph moving objects. To examine its unique ability and scope of application, the object oscillates with variable amplitude in horizontal axis, and the result via commonly used white noise are also measured as a comparison. We show that our method can image the object when the white noise method fails. In addition, our method uses less number of patterns, and enhances the signal-to-noise ratio (SNR) to a great extent.
We propose a computational ghost imaging scheme using customized pink noise speckle pattern illumination. By modulating the spatial frequency amplitude of the speckles, we generate speckle patterns with a significant positive spatial correlation. We experimentally reconstruct images using our synthesized speckle patterns in the presence of a variety of noise sources and pattern distortion and shown it is robust to noise interference. The results are compared with the use of standard white noise speckle patterns. We show that our method gives good image qualities under different noise interfere…
Computational ghost imaging or single-pixel imaging enables the image formation of an unknown scene using a lens-free photodetector. In this Letter, we present a computational panoramic ghost imaging system that can achieve the full-color panorama using a single-pixel photodetector, where a convex mirror performs the optical transformation of the engineered Hadamard-based circular illumination pattern from unidirectionally to omnidirectionally. To our best knowledge, it is the first time to propose the concept of ghost panorama and realize preliminary experimentations. It is foreseeable that g…
Classical ghost imaging is a computational imaging technique that employs patterned illumination. It is very similar in concept to the single-pixel camera in that an image may be reconstructed from a set of measurements even though all imaging quanta that pass through that sample are never recorded with a position resolving detector. The method was first conceived and applied for visible-wavelength photons and was subsequently translated to other probes such as x rays, atomic beams, electrons and neutrons. In the context of ghost imaging using penetrating probes that enable transmission measur…
Computational ghost imaging generally requires a large number of pattern illumination to obtain a high-quality image. The colored noise speckle pattern was recently proposed to substitute the white noise pattern in a variety of noisy environments and gave a significant signal-to-noise ratio enhancement even with a limited number of patterns. We propose and experimentally demonstrate here an orthonormalization approach based on the colored noise patterns to achieve sub-Nyquist computational ghost imaging. We tested the reconstructed image in quality indicators such as the contrast-to-noise rati…
Computational ghost imaging (CGI) and single-pixel camera (SPC) are two types of single-pixel imaging and attracts increasing interest in recent years. The performance differences of CGI and SPC in difference light disturbance environment are investigated. In comparison with CGI, we demonstrate that the quality of SPC is always better in the same conditions and local light disturbance has a larger influence to CGI/SPC than global light disturbance. In addition, a method to improve the reconstruction result of SPC is proposed if the source's energy is unstable and there is light disturbance in …
By encoding the high-dimensional light-field imaging information into a detectable two-dimensional speckle plane, ghost imaging camera via sparsity constraints (GISC camera) can directly catch the high-dimensional light-field imaging information with only one snapshot. This makes it worth to revisit the spatial resolution limit of this optical imaging system. In this paper we show both theoretically and experimentally that, while the resolution in high-dimensional light-field space is still limited by diffraction, the statistical spatial resolution of GISC camera can be greatly improved compar…
Ghost imaging (GI) is an unconventional imaging method that retrieves the image of an object by correlating a series of known illumination patterns with the total reflected (or transmitted) intensity. We here demonstrate a scheme which can remove the basic requirement of knowing the incident patterns on the object, enabling GI to non-invasively image objects through turbid media. As an experimental proof, we project a set of patterns towards an object hidden inside turbid media that scramble the illumination, making the patterns falling on the object completely unknown. We theoretically prove …
Computational ghost imaging (CGI) has recently been intensively studied as an indirect imaging technique. However, the speed of CGI cannot meet the requirements of practical applications. Here, we propose a novel CGI scheme for high-speed imaging. In our scenario, the conventional CGI data processing algorithm is optimized to a new compressed sensing (CS) algorithm based on a convolutional neural network (CNN). CS is used to process the data collected by a conventional CGI device. Then, the processed data are trained by a CNN to reconstruct the image. The experimental results show that our sch…
Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences（中国科学院上海光学精密机械研究所）
Ghost imaging radar is a new system of gaze imaging radar with high detection sensitivity, super-resolution and better anti-interference performance, but the relative motion between the radar system and the target will make the target imaging deteriorate. This paper proposes to perform absolute position localisation of a single target in the field of view by weighting the correlation strength of a single frame image of rough target, and to compensate translation of the reference arm speckle according to the localisation and tracking trajectory to accumulate the rough image into a high quality …
