Quantum enhanced non-interferometric quantitative phase imaging

Optimising detector readout settings for the detection of spatial correlations between SPDC photon-pairs

SPDC photon-pairs exhibit spatial correlations which can be measured using detector arrays sensitive to single photons. However, these detector arrays have multiple readout modes and in order to optimise detection it is important to select the optimum mode to detect the correlations against a background of optical and electronic noise. These quantum correlations enable applications in imaging, sensing, communication, and optical processing. Here we compare the measurement of spatial correlations for a broad range of readout modes of an EMCCD camera and attempt to characterise the optimal readout mode for our purposes. This assessment is important for the use of detector arrays of different types for use in quantum, low-light, enhanced resolution, imaging systems.

Generation of squeezed vacuum state in the millihertz frequency band

The detection of gravitational waves has ushered in a new era of observing the universe. Quantum resource advantages offer significant enhancements to the sensitivity of gravitational wave observatories. While squeezed states for ground-based gravitational wave detection have received marked attention, the generation of squeezed states suitable for mid-to-low-frequency detection has remained unexplored. To address the gap in squeezed state optical fields at ultra-low frequencies, we report on the first direct observation of a squeezed vacuum field until Fourier frequency of 4 millihertz with the quantum noise reduction of up to 8.0 dB, by the employment of a multiple noise suppression scheme. Our work provides quantum resources for future gravitational wave observatories, facilitating the development of quantum precision measurement.

Off-axis holographic imaging with undetected light

Quantum imaging with undetected light (QIUL) leverages the quantum correlations of photon pairs generated via spontaneous parametric down-conversion (SPDC) to retrieve both amplitude and phase information of an object. This method enables illumination and detection at distinct wavelength ranges, utilizing advanced detection technology in the visible spectrum while probing the object at an exotic wavelength. Here, we experimentally demonstrate a QIUL technique incorporating Fourier off-axis holography within a hybrid-type induced-coherence nonlinear interferometer. Our approach reconstructs the amplitude and phase information of an object in a single shot using a wide-field configuration, presenting a viable alternative to multi-frame acquisition techniques such as phase-shifting holography.

Analytical form of the refocused images from correlation plenoptic imaging

Correlation plenoptic imaging (CPI) is emerging as a promising approach to light-field imaging (LFI), a technique for concurrently measuring light intensity distribution and propagation direction of light rays from a 3D scene. LFI thus enables single-shot 3D imaging, offering rapid volumetric reconstruction. The optical performance of traditional LFI, however, is limited by a micro-lens array, causing a decline in resolution as 3D capabilities improve. CPI overcomes these limitation by measuring photon number correlations on two photodetectors with spatial resolution, in a lenslet-free design, so that the correlation function can be decoded in post-processing to reconstruct high-resolution images. In this paper, we derive the analytical expression of CPI images reconstructed through refocusing, addressing the previously unknown mathematical relationship between object shape and its final image. We show that refocused images are not limited by numerical aperture-induced blurring, as in conventional imaging. Rather, the image features of CPI can be explained through an analogy with imaging systems illuminated by spatially coherent light.

Hiding Images in Quantum Correlations

Photon-pair correlations in spontaneous parametric down-conversion are ubiquitous in quantum photonics. The ability to engineer their properties for optimizing a specific task is essential, but often challenging in practice. We demonstrate the shaping of spatial correlations between entangled photons in the form of arbitrary amplitude and phase objects. By doing this, we encode image information within the pair correlations, making it undetectable by conventional intensity measurements. It enables the transmission of complex, high-dimensional information using quantum correlations of photons, which can be useful for developing quantum communication and imaging protocols.

A comparison between the measurement of quantum spatial correlations using qCMOS photon-number resolving and electron multiplying CCD camera technologies

Cameras with single-photon sensitivities can be used to measure the spatial correlations between the photon-pairs that are produced by parametric down-conversion. Even when pumped by a single-mode laser, the signal and idler photons are typically distributed over several thousand spatial modes yet strongly correlated with each other in their position and anti-correlated in their transverse momentum. These spatial correlations enable applications in imaging, sensing, communication, and optical processing. Here we show that, using a photon-number resolving camera, spatial correlations can be observed after only a few 10s of seconds of measurement time, thereby demonstrating comparable performance with previous single photon sensitive camera technologies but with the additional capability to resolve photon-number. Consequently, these photon-number resolving technologies are likely to find wide use in quantum, low-light, imaging systems.

CauDR: A causality-inspired domain generalization framework for fundus-based diabetic retinopathy grading

Diabetic retinopathy (DR) is the most common diabetic complication, which usually leads to retinal damage, vision loss, and even blindness. A computer-aided DR grading system has a significant impact on helping ophthalmologists with rapid screening and diagnosis. Recent advances in fundus photography have precipitated the development of novel retinal imaging cameras and their subsequent implementation in clinical practice. However, most deep learning-based algorithms for DR grading demonstrate limited generalization across domains. This inferior performance stems from variance in imaging protocols and devices inducing domain shifts. We posit that declining model performance between domains arises from learning spurious correlations in the data. Incorporating do-operations from causality analysis into model architectures may mitigate this issue and improve generalizability. Specifically, a novel universal structural causal model (SCM) was proposed to analyze spurious correlations in fundus imaging. Building on this, a causality-inspired diabetic retinopathy grading framework named CauDR was developed to eliminate spurious correlations and achieve more generalizable DR diagnostics. Furthermore, existing datasets were reorganized into 4DR benchmark for DG scenario. Results demonstrate the effectiveness and the state-of-the-art (SOTA) performance of CauDR. Diabetic retinopathy (DR) is the most common diabetic complication, which usually leads to retinal damage, vision loss, and even blindness. A computer-aided DR grading system has a significant impact on helping ophthalmologists with rapid screening and diagnosis. Recent advances in fundus photography have precipitated the development of novel retinal imaging cameras and their subsequent implementation in clinical practice. However, most deep learning-based algorithms for DR grading demonstrate limited generalization across domains. This inferior performance stems from variance in imaging protocols and devices inducing domain shifts. We posit that declining model performance between domains arises from learning spurious correlations in the data. Incorporating do-operations from causality analysis into model architectures may mitigate this issue and improve generalizability. Specifically, a novel universal structural causal model (SCM) was proposed to analyze spurious correlations in fundus imaging. Building on this, a causality-inspired diabetic retinopathy grading framework named CauDR was developed to eliminate spurious correlations and achieve more generalizable DR diagnostics. Furthermore, existing datasets were reorganized into 4DR benchmark for DG scenario. Results demonstrate the effectiveness and the state-of-the-art (SOTA) performance of CauDR.

Birefringence mapping of biological tissues based on polarization sensitive non-interferometric quantitative phase imaging technique

OBJECTIVE

Oral cancer is a leading cause of mortality globally, particularly affecting developing regions where oral hygiene is often overlooked. The optical properties of tissues are vital for diagnostics, with polarization imaging emerging as a label-free, contrast-enhancing technique widely employed in medical and scientific research over past few decades.

MATERIALS AND METHODS

We present a novel polarization sensitive quantitative phase imaging of biological tissues by incorporating the conventional polarization microscope and transport of intensity equation-based phase retrieval algorithm. This integration provides access to the birefringence mapping of biological tissues. The inherent optical anisotropy in biological tissues induces the polarization dependent refractive index variations which can provide the detailed insights into the birefringence characteristics of their extracellular constituents. Experimental investigations were conducted on both normal and cancerous oral tissue samples by recording a set of three polarization intensity images for each case with a step size of 2 μm.

RESULTS

A noteworthy increment in birefringence quantification was observed in cancerous as compared to the normal tissues, attributed to the proliferation of abnormal cells during cancer progression. The mean birefringence values were calculated for both normal and cancerous tissues, revealing a significant increase in birefringence of cancerous tissues (2.1 ± 0.2) × 10-2 compared to normal tissues (0.8 ± 0.2) × 10-2. Data were collected from 8 patients in each group under identical experimental conditions.

CONCLUSION

This polarization sensitive non-interferometric optical approach demonstrated effective discrimination between cancerous and normal tissues, with various parameters indicating elevated values in cancerous tissues.