Adaptive ghost imaging

Robust compressed ghost imaging against environmental influence factors

Ghost imaging based on sparse sampling is sensitive to the environmental influence factors frequently encountered in practice, such as instrumental drift and ambient light change, which could cause degradation of image quality. In this manuscript, we report a robust compressed sensing technique which could effectively reduce the influence of measurement errors on image quality. For demonstration purposes, we implement the proposed technique to ghost imaging, namely differential compressed sensing ghost imaging (DCSGI). By applying differential measurements n times, the first n Taylor expansion polynomials of the error could be eliminated in n-order DCSGI. It has been verified theoretically and experimentally that DCSGI works well with typical errors which exists in the realities of ghost imaging applications, while the conventional approach can hardly. In addition, the proposed technique may also replace conventional compressed sensing in other applications for anti-interference high-quality reconstruction.

Study of computational sensing using frequency-domain compression

The computational sensing and imaging technique has been extended from spatial domain to temporal domain for capturing fast light signals with a slow photodetector. However, temporal computational sensing based on random source/modulation has to require a lot of measurements to reconstruct an object signal with acceptable SNR. In this paper, we study the frequency-domain acquisition technique for capturing a nanosecond temporal object with ten Hertz detection bandwidth. The frequency-domain acquisition technique offers a SNR gain of N, where N denotes the point number of Fourier spectrum. Because of the compressibility of data and the orthogonality and completeness of Fourier basis, it enables the reconstruction based on sub-Nyquist sampling. Because the slow detection only has low temporal resolution capability, the frequency-domain acquisition technique could provide robustness and is immune to the temporal distortion in experiments.

Semantic ghost imaging based on recurrent-neural-network

Ghost imaging (GI) illuminates an object with a sequence of light patterns and obtains the corresponding total echo intensities with a bucket detector. The correlation between the patterns and the bucket signals results in the image. Due to such a mechanism different from the traditional imaging methods, GI has received extensive attention during the past two decades. However, this mechanism also makes GI suffer from slow imaging speed and poor imaging quality. In previous work, each sample, including an illumination pattern and its detected bucket signal, was treated independently with each other. The correlation is therefore a linear superposition of the sequential data. Inspired by human's speech, where sequential words are linked with each other by a certain semantic logic and an incomplete sentence could still convey a correct meaning, we here propose a different perspective that there is potentially a non-linear connection between the sequential samples in GI. We therefore built a system based on a recurrent neural network (RNN), called GI-RNN, which enables recovering high-quality images at low sampling rates. The test with MNIST's handwriting numbers shows that, under a sampling rate of 1.28%, GI-RNN have a 12.58 dB higher than the traditional basic correlation algorithm and a 6.61 dB higher than compressed sensing algorithm in image quality. After trained with natural images, GI-RNN exhibits a strong generalization ability. Not only does GI-RNN work well with the standard images such as "cameraman", but also it can recover the natural scenes in reality at the 3% sampling rate while the SSIMs are greater than 0.7.

Image-enhanced single-pixel imaging using fractional calculus

Recent years, image enhancement for single-pixel imaging has developed rapidly and provides an image-free way for extracting image information. However, the conventional image enhancement approaches for single-pixel imaging are still based on the discontinuously adjustable operations such as integer-order derivatives, which are frequently used in edge detection but sensitive to the image noise. Therefore, how to balance between two conflicting demands, i.e. edge detection and noise suppression, is a new challenge. To address this issue, we introduce arbitrary-order fractional operations into single-pixel imaging. In experiment, the proposed technique has the capacity to detect image edges with high quality. Compared with integer-order derivative method which amplifies noise significantly while extracting edges, it offers a nice tradeoff between image SNR and performance of edge enhancement. In addition, it also shows good performance of image smoothing and improvement of image quality, if fractional order is negative. The proposed technique provides the adjustable fractional order as a new degree of freedom for edge extraction and image de-noising and therefore makes up for the shortcomings of traditional method for image enhancement.

High-resolution dynamic imaging system based on a 2D optical phased array

We propose an imaging system with scanning feedback of an optical phased array (OPA) for moving targets with unknown speed. The system combines OPA scanning velocimetry capability with OPA-based ghost imaging to enable trajectory tracking of targets moving within the field-of-view of the system while accomplishing image reconstruction. The proposed system can perform image reconstruction for millimeter-scale moving targets placed up to 20 m away from the camera. The system can be applied in areas such as autonomous driving and high-resolution imaging.

Fractional Fourier single-pixel imaging

Single-pixel imaging technology has a number of advantages over conventional imaging approaches, such as wide operation wavelength region, compressive sampling, low light radiation dose and insensitivity to distortion. Here, we report on a novel single-pixel imaging based on fractional Fourier transform (FRFT), which captures images by acquiring the fractional-domain information of targets. With the use of structured illumination of two-dimensional FRFT base patterns, FRFT coefficients of the object could be measured by single-pixel detection. Then, the object image is achieved by performing inverse FRFT on the measurements. Furthermore, the proposed method can reconstruct the object image from sub-Nyquist measurements because of the sparsity of image data in fractional domain. In comparison with traditional single-pixel imaging, it provides a new degree of freedom, namely fractional order, and therefore has more flexibility and new features for practical applications. In experiments, the proposed method has been applied for edge detection of object, with an adjustable parameter as a new degree of freedom.

Preventing forgery attacks in computational ghost imaging or disabling ghost imaging in a "spatiotemporal" scattering medium with weighted multiplicative signals

The ghost imaging (GI) approach is an intriguing and promising image acquisition technique that can transmit high-quality image information in a scattering environment. In this paper, we focus on two concerns recently emerged in the GI modality: one is the vulnerability to forgery attacks in GI-based optical encryption [Opt. Lett.45, 3917 (2020)OPLEDP0146-959210.1364/OL.392424], and the other is the potential threat of GI to personal privacy regarding non-invasive imaging [Opt. Express28, 17232 (2020)OPEXFF1094-408710.1364/OE.391788]. The core idea is to recommend introducing weighted multiplicative signals [Opt. Express27, 36505 (2019)OPEXFF1094-408710.1364/OE.27.036505] into the computational GI system, whether on the transmitting end or the receiving end. At the transmitting end, the random multiplicative signal can be used as an additional key that can reduce the possibility of forgery attacks, thereby increasing image transmission security. On the receiving end, the introduction of a random multiplicative signal to a spatial scattering medium makes it a "spatiotemporal" scattering medium, whose transmittance changes with time. Further, the spatiotemporal scattering medium can disable direct imaging and GI at the same time with low cost, thereby having great potential in privacy protection in daily lives.

Digital filtering ghost imaging to remove light disturbances

Ghost imaging (GI) can reconstruct the image of an object when the light traveling from the object to the detector is scattered or distorted. It is usually used in complicated environments, where the environmental light may heavily impact measurement. However, the traditional GI algorithm will be seriously affected if the environmental light changes during the measurement. In this paper, we analyze the frequency of environmental light and the light source, and introduce a digital filtering method that can improve the image quality of the traditional GI algorithm. Compared to the traditional GI algorithm, the digital filtering method can obtain an image even if the environmental light changes seriously.