Coherence Tomography Research Articles

Criteria for objects suitable for reconstruction from holograms and diffraction patterns

In this study, quantitative criteria for reconstruction of objects from their hologram and diffraction patterns, and in particular for the phase objects in digital holography, are derived. The criteria that allow distinguishing the hologram and diffraction pattern are outlined. Gabor derived his criterion for objects suitable for holography based on the condition that the background in the reconstructed object’s distribution should be nearly flat so that its intensity contrast does not exceed 0.05. According to Gabor, an opaque object is suitable for holographic reconstruction if it occupies no more than 1% of the imaged area, and a phase-shifting object cannot be reconstructed in principle. We revisit these criteria and show that both amplitude-only and phase-only objects can be reconstructed when the object occupies less than 1% of the total illuminated area. In addition, a simplified derivation of the criteria is provided that is based on Parseval’s theorem. It is shown that for objects (including amplitude-only and phase-only) reconstructed from their holograms and the twin image treated as noise, a signal-to-noise ratio of 10 or higher can be achieved provided the object occupies less than 0.5% of the total illuminated area. When a hologram is reconstructed by applying iterative algorithms, the requirement for the object size is much more generous and identical to that applied in coherent diffraction imaging: any type of object (amplitude-only, phase-only, or amplitude-and-phase mixed properties) is suitable for holography when the object’s size in each dimension is less than half of the probed region’s extent (or the field of view).

Imaging in-operando LiCoO2 nanocrystallites with Bragg coherent X-ray diffraction

Although the LiCoO2 (LCO) cathode material has been widely used in commercial lithium ion batteries (LIB) and shows high stability, LIB’s improvements have several challenges that still need to be overcome. In this paper, we have studied the in-operando structural properties of LCO within battery cells using Bragg Coherent X-ray Diffraction Imaging to identify ways to optimise the LCO batteries’ cycling. We have successfully reconstructed the X-ray scattering phase variation (a fingerprint of atomic displacement) within a ≈ (1.6 × 1.4 × 1.3) μm3 LCO nanocrystal across a charge/discharge cycle. Reconstructions indicate strained domains forming, expanding, and fragmenting near the surface of the nanocrystal during charging, with a determined maximum relative lattice displacements of 0.467 Å. While discharging, all domains replicate in reverse the effects observed from the charging states, but with a lower maximum relative lattice displacements of 0.226 Å. These findings show the inefficiency-increasing domain dynamics within LCO lattices during cycling.

High-Resolution Collaborative Forward-Looking Imaging Using Distributed MIMO Arrays

Airborne radar forward-looking imaging holds significant promise for applications such as autonomous navigation, battlefield reconnaissance, and terrain mapping. However, traditional methods are hindered by complex system design, azimuth ambiguity, and low resolution. This paper introduces a distributed array collaborative, forward-looking imaging approach, where multiple aircraft with linear arrays fly in parallel to achieve coherent imaging. We analyze signal model characteristics and highlight the limitations of conventional algorithms. To address these issues, we propose a high-resolution imaging algorithm that combines an enhanced missing-data iterative adaptive approach with aperture interpolation technique (MIAA-AIT) for effective signal recovery in distributed arrays. Additionally, a novel reference range cell migration correction (reference RCMC) is employed for precise range–azimuth decoupling. The forward-looking algorithm effectively transforms distributed arrays into a virtual long-aperture array, enabling high-resolution, high signal-to-noise ratio imaging with a single snapshot. Simulations and real data tests demonstrate that our method not only improves resolution but also offers flexible array configurations and robust performance in practical applications.

Characterization of Defocused Coherent Imaging Systems with Periodic Objects

Recent advancements in quantum and quantum-inspired imaging techniques have enabled high-resolution 3D imaging through photon correlations. These techniques exhibit reduced degradation of image resolution for out-of-focus samples compared to conventional methods (i.e., intensity-based incoherent imaging). A key advantage of these correlation-based approaches is their independence from the system numerical aperture (NA). Interestingly, both improved resolution of defocused images and NA-independent scaling are linked to the spatial coherence of light. This suggests that while correlation measurements exploit spatial coherence, they are not essential for achieving this imaging advantage. This discovery has led to the development of optical systems that achieve similar performance by using spatially coherent illumination and relying on intensity measurements: direct 3D imaging with NA-independent resolution was recently demonstrated in a correlation-free setup using LED light. Here, we explore the physics behind the enhanced performance of defocused coherent imaging, showing that it arises from the modification of the sample’s spatial harmonic content due to diffraction, unlike the blurring seen in conventional imaging. The results we present are crucial for understanding the implications of the physical differences between coherent and incoherent imaging, and are expected to pave the way for the practical application of the discovered phenomena.

W1-Net: a highly scalable ptychography convolutional neural network

X-ray ptychography is a coherent diffraction imaging technique that allows for the quantitative retrieval of both the amplitude and phase information of a sample in diffraction-limited resolution. However, traditional reconstruction algorithms require a large number of iterations to obtain phase and amplitude images exactly, and the expensive computation precludes real-time imaging. To solve the inverse problem of ptychography data, PtychoNN uses deep convolutional neural networks for real-time imaging. However, its model is relatively simple, and its accuracy is limited by the size of the training dataset, resulting in lower robustness. To address this problem, a series of W-Net neural network models have been proposed which can robustly reconstruct the object phase information from the raw data. Numerical experiments demonstrate that our neural network exhibits better robustness, superior reconstruction capabilities and shorter training time with high-precision ptychography imaging.

Learning infrared degradations for coherent visible image fusion in the undecimated dual-tree complex wavelet domain

Traditional infrared (IR) and visible (VIS) image fusion methods demand identical resolution levels for source images, which can be problematic due to the inherent low-resolution nature of IR imagery. In this paper, we introduce an innovative image fusion approach that harmonizes resolution across IR-VIS source images, leading to the generation of fused images with higher resolution. We employ a convolutional neural network model to recover the real-time image degradations in IR data, with a particular focus on super-resolution through the multi degradation resolution enhancement network (MDREN). We adopt undecimated dual-tree complex wavelet transform (UDT-CWT) in our fusion process due to its near shift invariance and better directionality capabilities. This results in coherent information of the fused images with minimized noise and loss. Experiments employing five image quality assessment measures are used to compare the proposed method to nine state-of-the-art approaches and show its efficacy.

Dynamic X-ray Coherent Diffraction Analysis: Bridging the Time Scales between Imaging and Photon Correlation Spectroscopy.

The advent of diffraction limited sources and developments in detector technology opens up new possibilities for the study of materials in situ and operando. Coherent X-ray diffraction techniques such as coherent X-ray diffractive imaging (CXDI) and X-ray photon correlation spectroscopy (XPCS) are capable for this purpose and provide complementary information, although due to signal-to-noise requirements, their simultaneous demonstration has been limited. Here, we demonstrate a strategy for the simultaneous use of CXDI and XPCS to study in situ the Brownian motion of colloidal gold nanoparticles of 200 nm diameter suspended in a glycerol-water mixture. We visualize the process of agglomeration, examine the spatiotemporal space accessible with the combination of techniques, and demonstrate CXDI with 22 ms temporal resolution.

Line-scan phase-resolved synthetic wavelength LiDAR using an off-axis holographic approach.

We demonstrate a novel, to our knowledge, approach for phase-resolved coherent 3D surface imaging that utilizes synthetic wavelength phase-based ranging and line-scan off-axis holography. Our proof-of-concept system employs an akinetic tunable laser to perform fast wavelength switching and a galvanometer mirror for slow-axis mechanical scanning. Quantitative depth measurements of an anodized aluminum plate and 3D-printed depth calibration targets and a printed circuit board are demonstrated. Analyses of both shot-noise limited system performance and speckle noise are also presented. The proof-of-concept system achieves micron-scale depth precision with a FOV of 12.8 mm × 34 mm and a 50 ms image acquisition time.

Synthesis of Arts by means of Sculpture Forms

The purpose of the work is to characterise the syncretism of sculptural forms in the development of monumental Ukrainian sculpture; emphasise the national features of the development of national sculpture on the example of the works of modern Ukrainian sculptors. The methodology of the work consists in the principles of holistic and systemic approaches to the study of the synthesis of arts in the sculptural forms of Ukrainian artists. An analytical method was applied to study scientific approaches to the synthesis of arts, as well as an artistic-stylistic method, as well as a system-functional method to the structuring of artistic works as a phenomenon of modern monumental sculpture. The scientific novelty of the study consists in the discourse on the synthesis of arts in the art history environment, characterising some works of art that belong to the category of monumental sculpture. Conclusions. It should be noted that in the Ukrainian art space there is a synthesis between classical forms and intermedia installations, which creates a complex and multi-layered context for modern artists. The perception of time, space and material becomes plastic and changeable, and this, in turn, stimulates the development of new aesthetic forms. Sculptural images have a significant influence on the formation of styles of young artists, where the emphasis is not only on silhouettes, but also on specific elements originating from various forms. Artists build associative visual sequences, embodying unrealistic, yet deeply symbolic images that resonate with viewers on an emotional level. In the examples from the workshops you mentioned, we see how a variety of materials and art forms and execution methods are used to create coherent and complete images that reflect the synthesis of different arts. The theme of the synthesis of arts in different sculptural forms, which sometimes seem completely incompatible, is indeed one of the most relevant for research.

Hyperspectral acquisition with ScanImage at the single pixel level: application to time domain coherent Raman imaging

We present a comprehensive strategy and its practical implementation using the commercial ScanImage software platform to perform hyperspectral point scanning microscopy when a fast time-dependent signal varies at each pixel level. In the proposed acquisition scheme, the scan along the X-axis is slowed down while the data acquisition is maintained at a high pace to enable the rapid acquisition of the time-dependent signal at each pixel level. The ScanImage generated raw 2D images have a very asymmetric aspect ratio between X and Y, the X axis encoding both for space and time acquisition. The results are X-axis macro-pixel where the associated time-dependent signal is sampled to provide hyperspectral information. We exemplified the proposed hyperspectral scheme in the context of time-domain coherent Raman imaging, where a pump pulse impulsively excites molecular vibrations that are subsequently probed by a time-delayed probe pulse. In this case, the time-dependent signal is a fast acousto-optics delay line that can scan a delay of 4.5ps in 25μs at each pixel level. With this acquisition scheme, we demonstrate ultra-fast hyperspectral vibrational imaging in the low frequency range [10cm−1, 150 cm−1] over a 500 μm field of view (64 x 64 pixels) in 130ms (∼ 7.5 frames/s). The proposed acquisition scheme can be readily extended to other applications requiring the acquisition of a fast-evolving signal at each pixel level.

Snapshot Coherent Diffraction Imaging via a Physics-embedded Untrained Neural Network

Snapshot Coherent Diffraction Imaging via a Physics-embedded Untrained Neural Network

Single-neuron representations of odours in the human brain

Olfaction is a fundamental sensory modality that guides animal and human behaviour1,2. However, the underlying neural processes of human olfaction are still poorly understood at the fundamental—that is, the single-neuron—level. Here we report recordings of single-neuron activity in the piriform cortex and medial temporal lobe in awake humans performing an odour rating and identification task. We identified odour-modulated neurons within the piriform cortex, amygdala, entorhinal cortex and hippocampus. In each of these regions, neuronal firing accurately encodes odour identity. Notably, repeated odour presentations reduce response firing rates, demonstrating central repetition suppression and habituation. Different medial temporal lobe regions have distinct roles in odour processing, with amygdala neurons encoding subjective odour valence, and hippocampal neurons predicting behavioural odour identification performance. Whereas piriform neurons preferably encode chemical odour identity, hippocampal activity reflects subjective odour perception. Critically, we identify that piriform cortex neurons reliably encode odour-related images, supporting a multimodal role of the human piriform cortex. We also observe marked cross-modal coding of both odours and images, especially in the amygdala and piriform cortex. Moreover, we identify neurons that respond to semantically coherent odour and image information, demonstrating conceptual coding schemes in olfaction. Our results bridge the long-standing gap between animal models and non-invasive human studies and advance our understanding of odour processing in the human brain by identifying neuronal odour-coding principles, regional functional differences and cross-modal integration.

Optical Coherence Tomography Image Analysis for Detection of Alzheimer’s Disease: A Comprehensive Structured Review

Optical Coherence Tomography (OCT) has emerged as a promising non-invasive imaging modality for the early detection of Alzheimer’s Disease (AD). This systematic literature review aims to consolidate current research on OCT image analysis for AD detection, addressing the growing need for early and accurate diagnostic tools. Despite the advances in neuroimaging, early diagnosis of AD remains challenging due to its asymptomatic nature in initial stages and the invasiveness of traditional methods. To achieve this, we conducted an extensive search of related articles from reputable databases (Scopus and Web of Science), focusing on studies published between 2022-2024. The flow of study was based on PRISMA framework. The database found (n = 29) final primary data. This review was divided into three themes, (1) retinal and ocular biomarkers for AD, (2) optical coherence tomography angiography (OCTA) and imaging techniques, and (3) machine learning and computational approaches for Alzheimer’s disease diagnosis. Key findings include the enlargement of the periarteriole capillary-free zone and changes in retinal nerve fibre layer thickness as potential AD biomarkers. Based on the review, the implementation of image analysis for OCT images have shown substantial potential for AD detection. By evaluating the past studies, gaps for the current research were discovered including the need for larger, more diverse cohorts and longitudinal studies to validate these biomarkers. In summary, detection of AD is possible through thorough OCT image analysis, but further research could be suggested to enhance its clinical applicability and reliability.

An optimal demodulation frequency band selection method based on spectral coherence fault energy factor for rolling bearing fault diagnosis

Identifying a frequency band with abundant fault information from the original vibration signal under the condition of complex background noise and the interference of other rotating parts is still problematic for envelope spectrum-based bearing diagnosis. To address this issue, a new indicator called spectral coherence fault energy factor (SCFE) to is proposed to select the optimal demodulation frequency band (ODFB) under complex noise interference for bearing fault diagnosis. Firstly, the fast spectral correlation (Fast-SC) is used to analyze the raw vibration signal and generate the Spectral Coherence (SCoh) image. Then, SCFE is employed to locate the initial center frequency from the whole carrier frequency, and the threshold function is used to optimize the center frequency and bandwidth adaptively to obtain ODFB. Finally, the envelope spectrum (ES) is used to identify bearing faults. The effectiveness of the developed method in identifying the fault related frequency band and bearing fault diagnosis is validated and compared using simulated signals with different interference noises. The results of experimental signals demonstrate that the developed indicator can efficiently evaluate bearing failure information, and the presented approach can accurately discriminate the failure-related frequency band and detect different bearing faults compared with the traditional methods.

Fourier Ptychographic Coherent Anti-Stokes Raman Scattering Microscopy with Point-Scanning for Super-Resolution Imaging.

Fourier ptychography (FP) is a high resolution wide-field imaging method based on the extended aperture in the Fourier space, which is synthesized from raw images with varying illumination angles. If FP is extended to coherent nonlinear optical imaging, the resolution could be further improved due to the increase of the cutoff frequency of the synthesized coherent optical transfer function (C-OTF) with respect to the order of nonlinear optical processes. However, there is a fundamental conflict between wide-field FP and nonlinear optical imaging, whereby the nonlinear optical imaging typically requires a focused excitation laser beam with high power density. To tackle the problem, in this work, a unique point-scanning FP (PS-FP) method is presented for super-resolution nonlinear optical imaging, in which the nonlinear optical signal is obtained by using focused laser beam, while the conventional FP algorithm can still be used to retrieve the super-resolution image. PS-FP coherent anti-Stokes Raman scattering (PS-FP-CARS) imaging on a variety of samples, where a 1.8-fold expansion of the OTF is achieved experimentally for enhancing vibrational imaging. Further theoretical calculation shows that the C-OTF of PS-FP higher-order CARS (PS-FP-HO-CARS) can be expanded up to ≈4.9-fold, thereby improving the spatial resolution by ≈3-fold in comparison with conventional point-scanning CARS with under tightly focused beams. The generality of PS-FP method developed in this work can be adapted to other coherent nonlinear optical imaging modalities for super-resolution imaging in tissue and cells.

Diffractive Optical Encryption Systems Based on Multiple Wavelengths and Multiple Distances

Coherent diffractive imaging is an optical methodology that encodes information about an object within the diffraction intensity. Here, we introduce a diffractive optical encryption system that utilizes multiple wavelengths and multiple distances, significantly expanding the size of the secret key space and enhancing the overall security of the system by incorporating these parameters as keys. The system adopts single optical path design, compact structure and is easy to implement, overcoming the disadvantage of single key space of traditional encryption system. This system can encrypt images into a series of diffraction intensity maps (i.e., ciphertexts), and exhibits a high sensitivity to minor variations in wavelength or distance during the process of decryption, showing excellent anti-cracking ability. Furthermore, the system also has considerable robustness, ensuring that the information still can be effectively recovered even in instances of partial loss. Numerical simulation results are presented to demonstrate feasibility and effectiveness of the proposed method. Our study provides novel concepts and methodologies to the advancement of optical encryption technology, while also offering significant technical assistance to the domain of information security.

Automated defect identification in coherent diffraction imaging with smart continual learning

Automated defect identification in coherent diffraction imaging with smart continual learning

SAR-NTV-YOLOv8: A Neural Network Aircraft Detection Method in SAR Images Based on Despeckling Preprocessing

Monitoring aircraft using synthetic aperture radar (SAR) images is a very important task. Given its coherent imaging characteristics, there is a large amount of speckle interference in the image. This phenomenon leads to the scattering information of aircraft targets being masked in SAR images, which is easily confused with background scattering points. Therefore, automatic detection of aircraft targets in SAR images remains a challenging task. For this task, this paper proposes a framework for speckle reduction preprocessing of SAR images, followed by the use of an improved deep learning method to detect aircraft in SAR images. Firstly, to improve the problem of introducing artifacts or excessive smoothing in speckle reduction using total variation (TV) methods, this paper proposes a new nonconvex total variation (NTV) method. This method aims to ensure the effectiveness of speckle reduction while preserving the original scattering information as much as possible. Next, we present a framework for aircraft detection based on You Only Look Once v8 (YOLOv8) for SAR images. Therefore, the complete framework is called SAR-NTV-YOLOv8. Meanwhile, a high-resolution small target feature head is proposed to mitigate the impact of scale changes and loss of depth feature details on detection accuracy. Then, an efficient multi-scale attention module was proposed, aimed at effectively establishing short-term and long-term dependencies between feature grouping and multi-scale structures. In addition, the progressive feature pyramid network was chosen to avoid information loss or degradation in multi-level transmission during the bottom-up feature extraction process in Backbone. Sufficient comparative experiments, speckle reduction experiments, and ablation experiments are conducted on the SAR-Aircraft-1.0 and SADD datasets. The results have demonstrated the effectiveness of SAR-NTV-YOLOv8, which has the most advanced performance compared to other mainstream algorithms.

Imaging of micro-steps on as-grown surface of sapphire with X-ray phase contrast technique

Basal-faceted sapphire ribbons grown using the Stepanov–LaBelle technology have a low density of surface steps. We present our results on a study of steps on the surface of a ribbon, misoriented relative to the singular face (0001) by several arc minutes. The ribbon has been characterized by means of in-line phase contrast imaging technique at Pohang Light Source, South Korea. It was shown for the first time that a step height of about 1 μm can be determined directly from an image. The step height obtained using the phase contrast method was confirmed by atomic force microscopy measurements. We have found that the experimental contrast matches the theoretical simulations only if the calculated intensity profile has been convolved with a Gaussian function. The full width at half maximum of the Gaussian was independently got from previous measurements. We have obtained an analytical solution in the case of theoretical fully coherent phase contrast image. The inverse problem is easy to solve, since there is a direct proportionality between the contrast and the step height.

A tunable spatial coherence imaging with ultrasonic array for defects in composites

ABSTRACT Ultrasonic phased array detection technology is a promising non-destructive testing (NDT) method that has been widely used for defect detection. Due to the high attenuation characteristics of ultrasonic wave propagation in carbon fibre reinforced polymer (CFRP) and the directivity of the array elements, the possibility of false or missed detection for defects that are far from the array elements or located very close together will increase. In addition, the total focusing method (TFM) employs only the defect information contained in the full matrix capture (FMC) dataset while ignoring the spatial information of the array signals, leading to image mixed with artefact and noise. An improved method called p-weighted-TFM (p-WTFM) is proposed. First, this method scales the magnitude of time-delayed channel signal by p-th root while maintaining the phase unchanged. Second, channel summation with energy compensation and directional correction will be realised. Finally, the signal dimensionality is restored by p-th power. Experimental results show that the p-WTFM can use any rational p-value to flexibly adjust image quality. Specially, this method can accurately distinguish adjacent defects spaced 1 mm apart with p value of 2.5, and it can also detect two defects located 10 mm deep with p value of 3.0.