Single-pixel imaging is suitable for low light-level scenarios because a bucket detector is used to maximally collect the light from an object. However, one of the challenges is its slow imaging speed, mainly due to the slow light-modulation technique. We here demonstrate 1.4-MHz video imaging based on computational ghost imaging with a red-green-blue light-emitting diode array having a full-range frame rate up to 100 MHz. With this method, the motion of a high-speed propeller is observed. Moreover, by use of single-photon detectors to increase the detection efficiency, this method is developed for ultrahigh-speed imaging under a low light level.

In turbid media, scattering of light scrambles information of the incident beam and represents an obstacle to optical imaging. Noninvasive imaging through opaque layers is especially challenging for reliable image reconstruction and dynamic objects. We here propose a solution to these problems: rather than using the full point-spread function or its Fourier transform (optical transfer function, OTF), the wave distortions in scattering layers can be characterized and diffraction-limited imaging performed using only the phase of OTF. Based on this understanding, we develop a method that exploits the redundant information from multiple measurements, which reliably yields OTF phases within several iterations. This method enables noninvasive imaging through turbid media with low signal-to-noise ratios in the measurements, which is not possible with previous methods. We then demonstrate noninvasive video imaging of a moving object hidden between scattering layers at 25–200 Hz. This imaging approach may inspire many other applications in scattering materials.

Deep-learning (DL) methods have gained significant attention in ghost imaging (GI) as promising approaches to attain high-quality reconstructions with limited sampling rates. However, existing DL-based GI methods primarily emphasize pixel-level loss and one-to-one mapping from bucket signals or low-quality GI images to high-quality images, tending to overlook the diversity in image reconstruction. Interpreting image reconstruction from the perspective of conditional probability, we propose the utilization of the denoising diffusion probabilistic model (DDPM) framework to address this challenge. Our designed method, known as DDPMGI, can not only achieve better quality but also generate reconstruction results with high diversity. At a sampling rate of 10%, our method achieves an average PSNR of 21.19 dB and an SSIM of 0.64, surpassing the performance of other comparison methods. The results of physical experiments further validate the effectiveness of our approach in real-world scenarios. Furthermore, we explore the potential application of our method in color GI reconstruction, where the average PSNR and SSIM reach 20.055 dB and 0.723, respectively. These results highlight the significant advancements and potential of our method in achieving high-quality image reconstructions in GI, including color image reconstruction.

We wish to report a recent experimental observation of anticorrelation from first-order and second-order incoherent thermal fields in the joint photodetection of two independent photodetectors. In the view of classical theory, the nontrivial second-order correlation of thermal light is caused by the statistical correlation of intensity fluctuations, which can be observed only from first-order coherent thermal radiations. What is the physical cause of this observed anticorrelation, then?

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.

Recently, ghost imaging has been attracting attention because its mechanism could lead to many applications inaccessible to conventional imaging methods. However, it is challenging for high-contrast and high-resolution imaging, due to its low signal-to-noise ratio (SNR) and the demand of high sampling rate in detection. To circumvent these challenges, we propose a ghost imaging scheme that exploits Haar wavelets as illuminating patterns with a bi-frequency light projecting system and frequency-selecting single-pixel detectors. This method provides a theoretically 100% image contrast and high-detection SNR, which reduces the requirement of high dynamic range of detectors, enabling high-resolution ghost imaging. Moreover, it can highly reduce the sampling rate (far below Nyquist limit) for a sparse object by adaptively abandoning unnecessary patterns during the measurement. These characteristics are experimentally verified with a resolution of $512\times 512$ and a sampling rate lower than 5%. A high-resolution ($1000\times 1000\times 1000$) 3D reconstruction of an object is also achieved from multi-angle images.