Quantum ghost imaging is an alternative imaging technique which utilises pairs of entangled photons to reconstruct an image. Due to the scanning nature of spatially resolving detectors and the inherent low light levels of quantum experiments, imaging speeds are inefficient and scale quadratically with the required resolution. We leveraged artificial intelligence capabilities to achieve early object recognition and to super-resolve the reconstructed image. We achieved a 5x reduction in image acquisition times and super-resolved the images to a resolution 4x greater than the measured resolution. Leading to efficient image acquisition times without losing fine details of the image.
Quantum ghost imaging is an alternative imaging technique which utilises pairs of entangled photons to reconstruct an image. Due to the scanning nature of spatially resolving detectors and the inherent low light levels of quantum experiments, imaging speeds are inefficient and scale quadratically with the required resolution. We leveraged artificial intelligence capabilities to achieve early object recognition and to super-resolve the reconstructed image. We achieved a 5x reduction in image acquisition times and super-resolved the images to a resolution 4x greater than the measured resolution. Leading to efficient image acquisition times without losing fine details of the image.
Quantum ghost imaging offers many advantages over classical imaging, including low photon fluxes and non-degenerate object and image wavelengths for imaging light sensitive structures, but suffers from slow image reconstruction speeds. Image reconstruction times depend on the resolution of the required image which scale quadratically with the image resolution. Here, we propose a super-resolved imaging approach based on neural networks where we reconstruct a low resolution image, which we denoise and super-resolve to a high resolution image. To test the approach, we implemented both a generative adversarial network as well as a super-resolving autoencoder in conjunction with an experimental quantum ghost imaging setup, demonstrating its efficacy across a range of object and imaging projective mask types. We achieved super-resolving enhancement of [Formula: see text] the measured resolution with a fidelity close to 90[Formula: see text] at an acquisition time of N[Formula: see text] measurements, required for a complete N [Formula: see text] N pixel image solution. This significant resolution enhancement is a step closer to a common ghost imaging goal, to reconstruct images with the highest resolution and the shortest possible acquisition time.
Quantum ghost imaging utilizes entangled photon pairs to enable an alternative image acquisition method. Information from either one of the photons does not allow for image reconstruction, however the image can be reconstructed by utilising the correlations that exist between the photon pair. Interestingly, these photon pairs can be either degenerate or non-degenerate in nature. Non-degenerate ghost imaging offers the ability to image with wavelength bandwidths where spatially resolving detectors are impractical, ineffective or expensive. Due to the scanning nature of spatially resolving detectors and the inherent low light levels of quantum experiments, imaging speeds are rather unsatisfactory. To overcome this limitation, we propose a two-step deep learning approach to establish an optimal early stopping point, tested on a non-degenerate system. In step one, we enhance the reconstructed image after each measurement by a deep convolution auto-encoder, followed by step two where a classifier is used to recognise the image. We achieved a recognition confidence of 75% at 20% of the image reconstruction time, hence reducing the image reconstruction time 5-fold while maintaining the image information. This, therefore, leads to a faster, more efficient image acquisition technique. Although tested on a non-degenerate system, our procedure can be extended to many such systems that are of quantum nature. We believe that this two-step deep learning approach will prove valuable to the community who are focusing their efforts on time-efficient ghost imaging.
Pairs of entangled photos are used to reconstruct an image in the application area known as quantum ghost imaging. It is the correlation between the photon pair that allows for the reconstruction of the image, as opposed to single photon detection. The entangled photons are spatially separated into two independent paths, one to illuminate the object and the other which is collected by a spatially resolving detector. Initially, ghost imaging experiments accomplished spatially resolving detectors by moving a single-pixel detector throughout a transverse scanning area. Advancements consisted of using ultra-sensitive cameras to avoid a system consisting of physically moving detectors. Ultra-sensitive cameras are, however, expensive and have limited spectral sensitivity. Here we demonstrate an alternative by utilising a spatial light modulator and a bucket detector to spatially resolve what is detected. Importantly, the masks displayed on the spatial light modulator must constitute a complete basis to acquire a completely reconstructed image. Historically, imaging speeds have been slow and inefficient due to the quadratic increase in the scanning capability for spatially resolved detectors and the low light levels associated with quantum experiments. Here we additionally utilise machine and deep learning algorithms to improve both image reconstruction time and resolution. We demonstrate this with a non-degenerate ghost imaging setup where the physical parameters such as the mask type and resolution are varied and controlled on a spatial light modulator. Thereby answering the question: can we image an object without using a camera?
The process of image recording is arguably one of the most prevalent technology in modern society and continues to inspire vast swathes of research due to its widespread applications spanning military, medical and consumer spheres. The danger present in a field so broad is that separate niches of research can become isolated with critical advancements struggling to traverse the gulfs between. Unifying the field is the omnipresent drive to acquire an ever increasing quality of images at the lowest possible cost, a goal which warrants continual fundamental research. Quantum entanglement exhibits many intriguing characteristics which make it a suitable tool for such fundamental investigations. The process of spontaneous parametric down- conversion has offered a yet unbeaten strength of photon correlations with the quantum nature of their production providing a reliable and controllable source of single-photons. Arising from these attributes is the technique known as ghost imaging. Though now known to be classically possible, the strength of entanglement generated correlations is yet to be surpassed. This thesis implemented this technique in tandem with the cutting edge detector technology in order to probe the fundamentals of image formation. This form of imaging allowed us to subject an object to a known number of photons whilst acquiring structural information from spatially separate, correlated photons which never interact with the object. The strength of the produced correlations allow us to acquire low background, high resolution images with far less light than traditional techniques and affords many novel benefits. The possibility of incorporating this technique with pre-existent regimes allowed me to draw from advancements made across the landscape of imaging research. Although referred to as “quantum ghost imaging” throughout this work, it should be noted that the intrinsic quantum nature of the correlations was not directly relied upon but provided an ideal source of strongly correlated photons. In order to determine the limits of a traditional imaging system this thesis first sought to answer the question: “can an image of an object be reconstructed from fewer photons than ii pixels in the image?” In chapter 3 I approached this from the perspective of compression, which minimises redundant information within a signal. This lead to the development of an imaging regime capable of imaging with far fewer photons than pixels in the image. By employing assumptions about the sparsity of natural images I was able to reconstruct an image of a biological sample containing an average of less than one photon per image pixel. Having reduced the number of photons necessary to form an image I then considered alternative methods for reducing the optical energy impinging on a sample. I sought to answer the question: “can non-degenerate ghost imaging reduce the optical energy impinged upon an object during imaging”. The photons produced in SPDC need not be of similar wavelengths, however may be chosen far from degeneracy, i.e. non-degenerate. In Chapter 4 I presented a ghost imaging system which illuminated the object with infrared light whilst recording the structural information via entangled visible photons. This allowed for objects opaque to visible light to be imaged in high quality without the need for a spatially resolving infrared detector, the state of the art of which lags behind their silicon based visible counterparts. I presented the systems capabilities by imaging objects which were etched into a gold substrate layered on to silicon, both of which are opaque to visible light. Not only did a reduction in energy deposition arise from the lower energy probe wavelength but applying the reconstruction techniques from the previous chapter brought that down to as low as ≈16 nJcm−2s−1. Seeking to expand the repertoire of applications, the low-light capabilities of my ghost imaging were applied to the technique of phase-contrast microscopy in chapter 5. Typically applied to translucent objects, phase-contrast imaging transfers phase information, i.e. the refractive index changed within the object, into an intensity distribution through the use of a phase-filter. In many of these applications the objects tend to be biological in nature, where high optical exposure can result in bleaching or damage. By applying the phase-filter non- locally, i.e. to the photons correlated to those probing the object, I acquired edge-enhanced images of a phase object whilst illuminating with significantly fewer photons than standard phase-contrast techniques. Having displayed the broad applicability of our low-light ghost imaging system, I then sought to determine the optical resolution in chapter 6. The resolution limits of ghost imaging are not clear at first glance owing to the resolutions dependence upon the strength of spatial correlations. As the length over which the spatial correlations are produced can be brought below the standard diffraction limit, it would seem the resolution of the system could be brought similarly low. To clarify this I artificially restricted the number of spatial modes in each of the correlated beams to uncover the physically realisable resolution. I show that although the resolution of a ghost imaging system to be fundamentally determined by the strength of the correlations, this can never be reached due to the inherent limitations of the intervening imaging system.
Quantum ghost imaging harnesses entangled photon pairs to form images using light that never directly interacts with the object. Conventional approaches rely on pixelated detectors or spatial masks, where the resolution is fundamentally limited by the pixel size and thus bounded by the Rayleigh criterion. In this work, we present a modal approach that replaces pixel-based detection with measurements in a basis of structured modes. By reconstructing images through tailored spatial modes, we decouple resolution from the physical limitations of detectors and instead tie it to the system’s optical precision and computational control. We demonstrate that even non-orthogonal modal sets can achieve high fidelity image reconstruction by exploiting modal sparsity, significantly reducing the number of required measurements. Using phase-only approximations of Hermite-Gaussian modes, we avoid the loss associated with complex amplitude modulation while retaining high reconstruction fidelity. The result is a high-resolution, high-efficiency quantum ghost imaging technique powered by structured light, opening new possibilities for photon-efficient imaging in low-light and quantum-limited regimes, such as biological microscopy and remote sensing.
The use of quantum properties of light has paved new avenues in the field of imaging. Quantum illumination and ghost-imaging protocols have shown exciting promise in addressing the issues inherent in classical imaging, such as background noise and a high level of required illumination. Moreover, quantum metrological schemes analyzing the modal content of light would address the resolution limits of direct imaging, promising enhancement beyond the Rayleigh limit. Augmenting these novel techniques with machine learning, particularly deep learning architectures such as Convolutional Neural Networks (CNNs), one strives to see significant improvements in image reconstruction and object identification in quantum imaging protocols. In my talk, I present recent progress and development on reconstructing the image of different objects with quantum sources, employing a high degree of spatial correlation between photon pairs and autoencoder deep learning architecture.