Nonlinear Imaging Research Articles

Multimedia image evaluation based on blockchain, visual communication design and color balance optimization

The relationship between image processing and image analysis is inseparable. With the increasing demand for multimedia visual images, the quality of image analysis is also increasing. However, in image processing and computer vision tasks, protecting users' privacy and preventing data leakage and abuse are not handled well. Image enhancement and nonlinear image color balance algorithm are applied to improve the visual quality of multimedia visual images and make them clearer and fuller. The article utilized image enhancement and a non-linear image color balance algorithm to improve the processing effect before visual image analysis. It also utilized the encryption mechanism of blockchain technology to detect the similarity of multimedia visual images. By comparing the feature points of the images, similar images were matched to address the copyright issue of the images. After experimental testing, the effect of image enhancement is significant, and the histogram of image equalization is significantly better than the original image. In the experiment of image analysis, the computer accurately classified visual images with different attributes. Finally, in the similarity detection algorithm of blockchain, the test results showed that when the number of image transactions reaches 500, the difference hash algorithm takes 1.13 s and 0.78 s to calculate the similarity comparison between the original and secondary images. The differential hash algorithm of blockchain is significantly superior to the Message-Digest Algorithm (MD5) in terms of computational speed and resource consumption. It has better image similarity detection performance and can also provide better image copyright protection mechanisms.

Super‐Resolution Two‐Photon Fluorescence Tomography Through the Phase‐Shifted Optical Beatings of Bessel Beams for High‐Resolution Deeper Tissue 3D Imaging

AbstractA novel z‐scanning‐free epi‐detected super‐resolution two‐photon fluorescence tomography (TPFT) technique enabling super‐resolution deeper tissue 3D imaging is reported. To accomplish this, a unique method is conceived by generating the phase‐shifted optical beatings of Bessel beams (PS‐OB3) with a spatial light modulator (SLM) to break the diffraction limit for enhancing both the lateral and axial resolutions as well as improving the penetration depth in TPFT for super‐resolution deeper tissue imaging. By electronically varying the optical beating frequency and the phase shifts of the beating patterns through SLM, the depth‐resolved TPF signals about the volumetric tissue are encoded in the spatial frequency domain and hence, a series of depth‐resolved TPF images can be retrieved by implementing inverse fast Fourier transform without a need of mechanical depth‐scanning. PS‐OB3 TPFT provides ≈1.3‐ and 2‐fold improvements in lateral and axial resolutions in comparison with conventional point‐scan TPF imaging. It is also illustrated that the epi‐detected PS‐OB3 TPFT imaging with inherent scattering‐resilient properties of the Bessel beams employed gives over 2‐fold improvement in imaging depth in porcine brain tissue compared to conventional point‐scan Gaussian beam TPF imaging. The z‐scanning‐free optical sectioning ability of PS‐OB3 method developed in TPFT is universal, which can be readily extended to practically any other nonlinear optical imaging modalities for super‐resolution deeper 3D imaging in biological and biomedical tissues.

Exploring the effect of photobiomodulation and gamma visual stimulation induced by 808 nm and visible LED in Alzheimer's disease mouse model

Although photobiomodulation (PBM) and gamma visual stimulatqion (GVS) have been overwhelmingly explored in the recent time as a possible light stimulation (LS) means of Alzheimer's disease (AD) therapy, their effects have not been assessed at once. In our research, the AD mouse model was stimulated using light with various parameters [continuous wave (PBM) or 40 Hz pulsed visible LED (GVS) or 40 Hz pulsed 808 nm LED (PBM and GVS treatment)]]. The brain slices collected from the LS treated AD model mice were evaluated using (i) fluorescence microscopy to image thioflavine-S labeled amy-loid-β (Aβ) plaques (the main hallmark of AD), or (ii) two-photon excited fluorescence (TPEF) imaging of unlabeled Aβ plaques, showing that the amount of Aβ plaques was reduced after LS treatment. The imaging results correlated well with the results of Morris water maze (MWM) test, which demonstrated that the spatial learning and memory abilities of LS treated mice were noticeably higher than those of untreated mice. The LS effect was also assessed by in vivo nonlinear optical imaging, revealing that the cerebral amyloid angiopathy decreased spe-cifically as a result of 40 Hz pulsed 808 nm irradiation, on the contrary, the angiopathy reversed after visible 40 Hz pulsed light treatment. The obtained results provide useful reference for further optimization of the LS (PBM or GVS) parameters to achieve efficient phototherapy of AD.

Generation and Manipulation of Spin‐Orbit Coupling mode via Four‐Wave Mixing with Quantum Interference

AbstractRecently, the vectorial nonlinear optical processes driven by spin‐orbit coupling (SOC) light have come to the fore, leading to striking optical phenomena and important applications both in classical and quantum optics. However, research on the SOC‐mediated light‐atom interactions is still in its infancy. Here, the generation and manipulation of SOC mode through a vectorial four‐wave mixing (FWM) process in 85Rb vapor is demonstrated. Under the excitation of two SOC pump beams, multiple FWM paths can be simultaneously established due to the rich atomic Zeeman sublevels coupling with different circularly polarized components of SOC fields. A higher‐order cylindrical or more general SOC FWM signal is observed in the vectorial FWM process, which obeys angular momentum conservation and Gouy phase matching. In particular, quantum interference between different FWM paths plays a crucial role in manipulating the SOC mode of the generated FWM signal, revealing that the conversion of SOC mode is intrinsically quantum. This work provides a pathway toward deeper insight into the vectorial nonlinear optical processes and may advance technology for shaping spatially structured light, which is essential in applications such as nonlinear polarization imaging and the optical communication realm.

A Simple Nonlinear Classifier Using a Multimode Optical Chip

Neural network accelerator based on photonic‐integrated circuits is emerging as a promising technology for fast, power‐efficient, and parallel computing. Under such technology, optics is mainly used for linear transformations, e.g., through an array of cascaded switching networks. Nonlinear activation is implemented either electronically requiring extra optical–electrical conversion or via nonlinear optical materials that often suffer from high loss, large power consumption, and difficulty in integration. Herein, an optical neural chip with only one multimode waveguide, fabricated using low‐cost linear optical materials, plus seven heater electrodes to control the multimode interference, is proposed. The nonlinear networks are intrinsically integrated in the electrical‐to‐optical signal conversion through the waveguide. The linear computation, in the electronic domain, is included in the mandatory step to convert the input matrix to the intermediate current values on the seven electrodes. Though extremely simple, the proposed system can classify nonlinear datasets and images by optical readout with high accuracy and without calibration. Prospects for future development are given at the end. In this work, an alternative route is offered to exploiting the classic multimode interference for advanced optical computing applications.

Advances in nonlinear metasurfaces for imaging, quantum, and sensing applications

Abstract Metasurfaces, composed of artificial meta-atoms of subwavelength size, can support strong light–matter interaction based on multipolar resonances and plasmonics, hence offering the great capability of empowering nonlinear generation. Recently, owing to their ability to manipulate the amplitude and phase of the nonlinear emission in the subwavelength scale, metasurfaces have been recognized as ultra-compact, flat optical components for a vast range of applications, including nonlinear imaging, quantum light sources, and ultrasensitive sensing. This review focuses on the recent progress on nonlinear metasurfaces for those applications. The principles and advances of metasurfaces-based techniques for image generation, including image encoding, holography, and metalens, are investigated and presented. Additionally, the overview and development of spontaneous photon pair generation from metasurfaces are demonstrated and discussed, focusing on the aspects of photon pair generation rate and entanglement of photon pairs. The recent blossoming of the nonlinear metasurfaces field has triggered growing interest to explore its ability to efficiently up-convert infrared images of arbitrary objects to visible images and achieve spontaneous parametric down-conversion. This recently emerged direction holds promising potential for the next-generation technology in night-vision, quantum computing, and biosensing fields.

Nonlinear image encryption based on phase truncation and phase retrieval operation

Nonlinear image encryption based on phase truncation and phase retrieval operation

Analysis of osteoporotic tissue using combination nonlinear optical imaging

Currently, a large number of stored tissue samples are unavailable for spectroscopic study without the time consuming and destructive process of paraffin removal. Instead, a structurally sensitive technique, sum frequency generation, and a chemically sensitive technique, coherent anti-Stokes Raman scattering enables imaging through the paraffin. This method is demonstrated by imaging collagen in mouse tibia. We introduce a statistical method for separating images by quality and, with the aid of machine learning, distinguish osteoporotic and healthy bone. This method has the potential to verify the results of previous studies and reduce new sample production by allowing retesting results with spectroscopy.

Coarse-to-fine matching via cross fusion of satellite images

The registration of multimodal satellite images is essential for a prerequisite for accruing complementary observational data. Nevertheless, the differential imaging nuances amongst non-linear radiometric multimodal images precipitate a complexity in keypoint detection, rendering it a great challenge. This complexity exacerbates the difficulty encountered in matching multimodal satellite images. In this paper, a dual-branch cross fusion network (DF-Net) is proposed for the purpose of satellite image registration. DF-Net relies on the self-attention granted to a pair of images, thereby providing cross-modal fusion feature descriptions. Initially, reference and sensed images are deployed as inputs for the dual-branch network, which in turn engenders feature descriptions of both high and low resolution, respectively. Sequentially, the matching of individual feature descriptions is anchored on the low-resolution feature map, paving the way for the establishment of coarse matching correspondences. Subsequently, the outcomes of these coarse correspondences are transposed onto the feature map with a higher resolution, thereby generating fine matching results for each coarse correspondence. An exhaustive set of qualitative and quantitative assessments have been administered on three satellite image datasets encompassing a diverse range of scenarios. The average Repeatability (Rep.), Mean Matching Accuracy (MMA), and Root-Mean-Square Error (RMSE) of the DF-Net applied to three large-scale satellite images were recorded to be 0.71, 0.65, and 2.34, respectively. These findings buttress the proficiency of the proposed strategy in facilitating cross-modal matching and bear testimony to the sterling performance of the method proposed.

Proline catabolism is a key factor facilitating Candida albicans pathogenicity.

Candida albicans, the primary etiology of human mycoses, is well-adapted to catabolize proline to obtain energy to initiate morphological switching (yeast to hyphal) and for growth. We report that put1-/- and put2-/- strains, carrying defective Proline UTilization genes, display remarkable proline sensitivity with put2-/- mutants being hypersensitive due to the accumulation of the toxic intermediate pyrroline-5-carboxylate (P5C), which inhibits mitochondrial respiration. The put1-/- and put2-/- mutations attenuate virulence in Drosophila and murine candidemia models and decrease survival in human neutrophils and whole blood. Using intravital 2-photon microscopy and label-free non-linear imaging, we visualized the initial stages of C. albicans cells infecting a kidney in real-time, directly deep in the tissue of a living mouse, and observed morphological switching of wildtype but not of put2-/- cells. Multiple members of the Candida species complex, including C. auris, are capable of using proline as a sole energy source. Our results indicate that a tailored proline metabolic network tuned to the mammalian host environment is a key feature of opportunistic fungal pathogens.

Automated Detection of Corneal Edema With Deep Learning-Assisted Second Harmonic Generation Microscopy

Second Harmonic Generation Microscopy (SHG) is widely acknowledged as a valuable non-linear optical imaging tool, its contrast mechanism providing the premises to non-invasively identify, characterize, and monitor changes in the collagen architecture of tissues. However, the interpretation of SHG data can pose difficulties even for experts histopathologists, which represents a bottleneck for the translation of SHG-based diagnostic frameworks to clinical settings. The use of artificial intelligence methods for automated SHG analysis is still in an early stage, with only few studies having been reported to date, none addressing ocular tissues yet. In this work we explore the use of three Deep Learning models, the highly popular InceptionV3 and ResNet50, alongside FLIMBA, a custom developed architecture, requiring no pre-training, to automatically detect corneal edema in SHG images of porcine cornea. We observe that Deep Learning models building on different architectures provide complementary results for the classification of cornea SHG images and demonstrate an AU-ROC = 0.98 for their joint use. These results have potential to be extrapolated to other diagnostics scenarios, such as automated extraction of hydration level of cornea, or identification of corneal edema causes, and thus pave the way for novel methods for precision diagnostics of the cornea with Deep-Learning assisted SHG imaging.

Terahertz wave imaging using time domain iterative shrink threshold method

We propose a new linear imaging method that combines the iterative shrinkage thresholding algorithm (ISTA) with the synthetic aperture (SA) method to achieve high resolution. Unlike other ISTA methods that use a single frequency component of signals, we use time domain signals directly as measurement, which allows for simultaneous wide bandwidth calculation and potentially better imaging results. Additionally, we simplify the backward estimation process by using impulse back-projection based on the SA method, which reduces the computing cost compared to the ISTA method. Numerical simulation and experimental measurement results show that the image resolution is similar to that of our previous non-linear imaging method, and two reflectors with different reflectivity are imaged linearly. These comparison results demonstrate the effectiveness of our proposed method in achieving high-resolution and maintaining the linearity of target reflectivity.

Non-linear statistical image watermark detector

Non-linear statistical image watermark detector

Leveraging the periodic interference condition in electro-optic modulators for picosecond pulse generation

Ultra-short optical pulses in the femtosecond and picosecond regime are typically generated using mode-locked lasers. However, in mode-locking, the pulse repetition rate is fundamentally linked to the cavity length of the laser, making it difficult to synchronize these laser pulses to other light sources. Here, we apply a pulse-on-demand approach to picosecond pulse generation with an electro-optic intensity modulator (EOM). The high, 40 GHz bandwidth of the EOM enables low picosecond pulses, however it shifts the problem of pulse generation to the electronic pulses, requiring high bandwidth electronics. In this study, we present an electro-optic operation, leveraging the periodic interference condition of intensity EOMs by operating it with rising edges at twice its V π voltage. Utilizing this method, pulse durations as short as 10.9 ps were achieved by employing a 35 ps edge from an arbitrary waveform generator. The pulses were measured directly on a high-speed oscilloscope as well as indirectly through the spectral broadening of the generated optical pulses. We employ this approach to show arbitrary pulse length generation by applying step functions with only one V π voltage, thus permitting direct pulse-on-demand generation of pulses with arbitrary pulse length, shape and repetition rate for applications in spectroscopy, sensing and nonlinear imaging.

Insights into solid dosage forms with nonlinear optical imaging

Microscopic chemical and solid-state structures and their changes in solid drugs and dosage forms can profoundly affect pharmaceutical performance and patient safety. Despite this, their detailed spatially-resolved analysis can be difficult or impossible with established analytical technologies. Multimodal non-linear optical imaging represents opportunities for sensitive and specific chemical and solid-state pharmaceutical imaging. Non-linear optical imaging encompasses several nonlinear optical phenomena, including coherent anti-Stokes Raman scattering (CARS), stimulated Raman scattering (SRS), and sum frequency/second harmonic generation (SFG/SHG). Imaging in 3D with (sub)micron resolution is rapid, non-destructive, possible in situ in aqueous media, and generally does not require prior sample preparation. This mini-review explores several applications of non-linear optical imaging for solid drug and dosage form analysis.

Quantifying U-Net uncertainty in multi-parametric MRI-based glioma segmentation by spherical image projection.

Uncertainty quantification in deep learning is an important research topic. For medical image segmentation, the uncertainty measurements are usually reported as the likelihood that each pixel belongs to the predicted segmentation region. In potential clinical applications, the uncertainty result reflects the algorithm's robustness and supports the confidence and trust of the segmentation result when the ground-truth result is absent. For commonly studied deep learning models, novel methods for quantifying segmentation uncertainty are in demand. To develop a U-Net segmentation uncertainty quantification method based on spherical image projection of multi-parametric MRI (MP-MRI) in glioma segmentation. The projection of planar MRI data onto a spherical surface is equivalent to a nonlinear image transformation that retains global anatomical information. By incorporating this image transformation process in our proposed spherical projection-based U-Net (SPU-Net) segmentation model design, multiple independent segmentation predictions can be obtained from a single MRI. The final segmentation is the average of all available results, and the variation can be visualized as a pixel-wise uncertainty map. An uncertainty score was introduced to evaluate and compare the performance of uncertainty measurements. The proposed SPU-Net model was implemented on the basis of 369 glioma patients with MP-MRI scans (T1, T1-Ce, T2, and FLAIR). Three SPU-Net models were trained to segment enhancing tumor (ET), tumor core (TC), and whole tumor (WT), respectively. The SPU-Net model was compared with (1) the classic U-Net model with test-time augmentation (TTA) and (2) linear scaling-based U-Net (LSU-Net) segmentation models in terms of both segmentation accuracy (Dice coefficient, sensitivity, specificity, and accuracy) and segmentation uncertainty (uncertainty map and uncertainty score). The developed SPU-Net model successfully achieved low uncertainty for correct segmentation predictions (e.g., tumor interior or healthy tissue interior) and high uncertainty for incorrect results (e.g., tumor boundaries). This model could allow the identification of missed tumor targets or segmentation errors in U-Net. Quantitatively, the SPU-Net model achieved the highest uncertainty scores for three segmentation targets (ET/TC/WT): 0.826/0.848/0.936, compared to 0.784/0.643/0.872 using the U-Net with TTA and 0.743/0.702/0.876 with the LSU-Net (scaling factor=2). The SPU-Net also achieved statistically significantly higher Dice coefficients, underscoring the improved segmentation accuracy. The SPU-Net model offers a powerful tool to quantify glioma segmentation uncertainty while improving segmentation accuracy. The proposed method can be generalized to other medical image-related deep-learning applications for uncertainty evaluation.

Laser nanoprinting of 3D nonlinear holograms beyond 25000 pixels-per-inch for inter-wavelength-band information processing

Nonlinear optics provides a means to bridge between different electromagnetic frequencies, enabling communication between visible, infrared, and terahertz bands through χ(2) and higher-order nonlinear optical processes. However, precisely modulating nonlinear optical waves in 3D space remains a significant challenge, severely limiting the ability to directly manipulate optical information across different wavelength bands. Here, we propose and experimentally demonstrate a three-dimensional (3D) χ(2)-super-pixel hologram with nanometer resolution in lithium niobate crystals, capable of performing advanced processing tasks. In our design, each pixel consists of properly arranged nanodomain structures capable of completely and dynamically manipulating the complex-amplitude of nonlinear waves. Fabricated by femtosecond laser writing, the nonlinear hologram features a pixel diameter of 500 nm and a pixel density of approximately 25000 pixels-per-inch (PPI), reaching far beyond the state of the art. In our experiments, we successfully demonstrate the novel functions of the hologram to process near-infrared (NIR) information at visible wavelengths, including dynamic 3D nonlinear holographic imaging and frequency-up-converted image recognition. Our scheme provides a promising nano-optic platform for high-capacity optical storage and multi-functional information processing across different wavelength ranges.

Interaction of colliding laser pulses with gas plasma for broadband coherent terahertz wave generation

Colliding of two counter-propagating laser pulses is a widely used approach to create a laser field or intensity surge. We experimentally demonstrate broadband coherent terahertz (THz) radiation generation through the interaction of colliding laser pulses with gas plasma. The THz radiation has a dipole-like emission pattern perpendicular to the laser propagation direction with a detected peak electric field 1 order of magnitude higher than that by single pulse excitation. As a proof-of-concept demonstration, it provides a deep insight into the physical picture of laser–plasma interaction, exploits an important option to the promising plasma-based THz source, and may find more applications in THz nonlinear near-field imaging and spectroscopy.

Imaging and component analysis of pumpkin stem tissue with simultaneous SF-CARS and TPEF microscopy.

A multimodal nonlinear optical imaging platform based on a single femtosecond oscillator is built for simultaneous TPEF and SF-CARS imaging. TPEF microscopy and SF-CARS microscopy is utilized for mapping the distribution of the lignin component and the polysaccharide component, respectively. Visualization of vessel structure is realized. And the relative distribution of lignin and polysaccharide of vessel structure is mapped. Two pumpkin stem tissue areas with different degrees of lignification are observed with simultaneous TPEF and SF-CARS imaging, and two types of cell walls are identified. The different distribution patterns of lignin and polysaccharide in these two types of cell walls, induced by different degrees of lignification, are analyzed in detail.

Effects of chirp of the white-light continuum seed on the pulse characteristics of collinear femtosecond optical parametric amplifiers

The influence of the chirp of the white-light continuum seed on the pulse characteristics of femtosecond optical parametric amplifiers (OPAs) is studied. Here, we focus on collinear OPAs based on type-I phase-matched BBO crystals. By numerical simulations, we investigate the evolution of the pulse-width, spectral bandwidth and time-bandwidth-product (TBP) of the amplified signal pulses with respect to the chirp of the white-light continuum seed pulses. In particular, we show that by manipulating the white-light continuum seed pulses to be highly chirped, nearly transform-limited optical pulses can be directly obtained from OPAs, without requiring dispersion-compensation elements. Our study demonstrates that chirp management offers a concise and effective technical route for obtaining nearly transform-limited, narrowband optical pulses from femtosecond OPAs, which would benefit many applications, including nonlinear imaging and spectroscopy.