Conventional imaging at low light levels requires hundreds of detected photons per pixel to suppress the Poisson noise for accurate reflectivity inference. We propose a high-efficiency photon-limited imaging technique, called fast first-photon ghost imaging, which recovers the image by conditional averaging of the reference patterns selected by the first-photon detection signal. Our technique merges the physics of low-flux measurements with the framework of computational ghost imaging. Experimental results demonstrate that it can reconstruct an image from less than 0.1 detected photon per pixel, which is three orders of magnitude less than conventional imaging techniques. A signal-to-noise ratio model of the system is established for noise analysis. With less data manipulation and shorter time requirements, our technique has potential applications in many fields, ranging from biological microscopy to remote sensing.

Imaging under ultra-weak light conditions is affected by Poisson noise heavily. The problem becomes worse if a scattering media is present in the optical path. Speckle patterns detected under ultra-weak light condition carry very little information which makes it difficult to reconstruct the image. Off-the-shelf methods are no longer available in this condition. In this paper, we experimentally demonstrate the use of a deep learning network to reconstruct images through scattering media under ultra-weak light illumination. The weak light limitation of this method is analyzed. The random Poisson detection under weak light condition obtains partial information of the object. Based on this property, we demonstrated better performance of our method by enlarging the training dataset with multiple detections of the speckle patterns. Our results demonstrate that our approach can reconstruct images through scattering media from close to $1$ detected signal photon per pixel (PPP) per image.

We propose the polarization difference ghost imaging method and experimentally demonstrate that polarization properties can provide additional information in conventional ghost imaging for object discrimination with contrast enhancement. In our experiment, two kinds of visually similar objects with different polarization properties can be separated for imaging. Meanwhile, an improved polarization difference algorithm is presented, fully utilizing the polarization discrepancy between objects and background, to further enhance the image contrast. Our work facilitates practical applications of ghost imaging.

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.

With one single photon camera (SPC), imaging under ultra weak-lighting conditions may have wide-ranging applications ranging from remote sensing to night vision, but it may seriously suffer from the problem of under-sampled inherent in SPC detection. Some approaches have been proposed to solve the under-sampled problem by detecting the objects many times to generate high-resolution images and performing noise reduction to suppress the Poission noise inherent in low-flux operation. To address the under-sampled problem more effectively, a new approach is developed in this paper to reconstruct high-resolution images with lower-noise by seamlessly integrating low-light-level imaging with deep learning. In our new approach, all the objects are detected only once by SPC, where a deep network is learned to reduce noise and reconstruct high-resolution images from the detected noisy under-sampled images. In order to demonstrate our proposal is feasible, we first select a special category to verify by experiment, which are human faces. Such deep network is able to recover high-resolution and lower-noise face images from new noisy under-sampled face images and the resolution can achieve 4× up-scaling factor. Our experimental results have demonstrated that our proposed method can generate high-quality images from only ~0.2 detected signal photon per pixel.

Ghost imaging is a transverse imaging technique that allows an object to be reconstructed using the correlation between a pair of light fields. As known, in ghost imaging configurations, a large number of realizations are usually required for reconstruction of the objects. To reduce the number of realizations, Chen et al. [Opt. Lett. 38, 546–548 (2013) [CrossRef] ] demonstrated an object authentication method with computational ghost imaging using realizations of less than 5% of the Nyquist limit. In this paper, we have further improved this method using a “compressive sensing algorithm” instead of a “classical correlation algorithm in computational ghost imaging.” As a result, the realizations for object authentication were further reduced from 5% of the Nyquist limit to 3% of the Nyquist limit.