Spectral Filtering Research Articles

AD-Graph: Weakly Supervised Anomaly Detection Graph Neural Network

The main challenge faced by video-based real-world anomaly detection systems is the accurate learning of unusual events that are irregular, complicated, diverse, and heterogeneous in nature. Several techniques utilizing deep learning have been created to detect anomalies, yet their effectiveness on real-world data is often limited due to the insufficient incorporation of motion patterns. To address these problems and enhance the traditional functionality of anomaly detection systems for surveillance video data, we propose a weakly supervised graph neural-network-assisted video anomaly detection framework called AD-Graph. To identify temporal information from a series of frames, we extract 3D visual and motion features and represent these in a language-based knowledge graph format. Next, a robust clustering strategy is applied to group together meaningful neighbourhoods of the graph with similar vertices. Furthermore, spectral filters are applied to these graphs, and spectral graph theory is used to generate graph signals and detect anomalous events. Extensive experimental results over two challenging datasets, UCF-Crime and ShanghaiTech, show improvements of 0.35% and 0.78% against a state-of-the-art model.

Frequency-encoded two-photon excited fluorescence microscopy.

Two-photon excited fluorescence (2PEF) microscopy is the most popular non-linear imaging method of biomedical samples. State-of-the art 2PEF microscopes use multiple detectors and spectral filter sets to discriminate different fluorophores based on their distinct emission behavior (emission discrimination). One drawback of 2PEF is that fluorescence photons outside the filter transmission range are inherently lost, thereby reducing the imaging efficiency and speed. Furthermore, emission discrimination of different fluorophores may fail if their emission profiles are too similar. Here, we present an alternative 2PEF method that discriminates fluorophores based on their excitation spectra (excitation discrimination). For excitation we use two lasers of different wavelengths (ω1, ω2) resulting in excitation energies at 2ω1, 2ω2, and the mixing energy ω1+ω2. Both lasers are frequency encoded (FE) by an intensity modulation at distinct frequencies while all 2PEF emission is collected on a single detector. The signal is fed into a lock-in-amplifier and demodulated at various frequencies simultaneously. A customized nonnegative matrix factorization (NNMF) then generates fluorescence images that are free of cross talk. Combining FE-2PEF with multiple detectors has the potential to enable the simultaneous imaging of an unprecedented number of fluorophores.

Graph neural network-based remote target classification in hyperspectral imaging

ABSTRACT Hyperspectral Imagery (HSI) onboard target classification is a frequently used technique in remote sensing. Due to the small volume of labelled data, the classification of HSI has been a challenging subject in recent study. The graph neural network (GNN) has become a popular method for semi-supervised classification, and its use with hyperspectral images has gained a lot of interest. A graph filter or single graph neural network has been typically employed in the earlier GNN-based approaches, and it is used to obtain HSI characteristics. It has not fully utilized the benefits of different graph filters or graph neural networks. The issue of oversmoothing also persists with the classical GNNs. We propose a spectral filter and an autoregressive moving average filter for the multi-graph neural network; (SAF-GNN) to address such drawbacks as the one mentioned above. The first is excellent at extracting the nodes’ spectral characteristics, and the second is effective at suppressing graph distortion. With the help of performance measures like overall accuracy (OA), individual class accuracy (ICA), and Kappa coefficient (KC), the performance of the proposed method was compared to that of other state-of-the-art methods. The result shows that the SAF-GNN provides an improvement in OA, ICA and KC of 2.21%, 7.93% and 0.025 for the cuprite dataset and 4.84%, 11.78% and 0.025 for the Pavia University dataset, respectively. Comparison results show that the proposed method is suitable for remote target classification problem.

Calibration of the SNPP and NOAA 20 VIIRS sensors for continuity of the MODIS climate data records

Accurate long-term sensor calibration and periodic re-processing to ensure consistency and continuity of atmospheric, land and ocean geophysical retrievals from space within the mission period and across different missions is a major requirement of climate data records. In this work, we applied the Multi-Angle Implementation of Atmospheric Correction (MAIAC)-based vicarious calibration technique over Libya-4 desert site to perform calibration analysis of Visible Infrared Imaging Radiometer Suite (VIIRS) on Suomi National Polar-orbiting Partnership (SNPP) and NOAA-20 satellites. For both VIIRS sensors we characterized residual linear calibration trends and cross-calibrated both sensors to MODerate resolution Imaging Spectroradiometer (MODIS) Aqua regarded as a calibration standard. The relative spectral response (RSR) differences were accounted for using the German Aerospace Center (DLR) Earth Sensing Imaging Spectrometer (DESIS) hyperspectral surface reflectance data. Our results agree with independent vicarious calibration results of both the MODIS/VIIRS Characterization Support Team as well as the CERES Imager and Geostationary Calibration Group within estimated uncertainty of 1–2%. Analysis of MAIAC geophysical products with the new calibration shows a high level of agreement of MAIAC aerosol, surface reflectance and NDVI records between MODIS and VIIRS. Excluding high aerosol optical depth (AOD), all three sensors agree in AOD with mean difference (MD) less than 0.01 and residual mean squared difference rmsd ∼ 0.04. Spectral geometrically normalized surface reflectance agrees within rmsd of 0.003–0.005 in the visible and 0.01–0.012 at longer wavelengths. The residual surface reflectance differences are fully explained by differences in spectral filter functions. Finally, difference in NDVI is characterized by rmsd ∼ 0.02 and MD less than 0.003 for NDVI based on VIIRS imagery bands I1/I2 and less than 0.01 for NDVI based on VIIRS radiometric bands M5/M7. In practical sense, these numbers indicate consistency and continuity in MAIAC records ensuring the smooth transition from MODIS to VIIRS.

Capability for dose reduction while maintaining nodule detection: Comparison of silver and copper X-ray spectrum modulation filters for chest CT using a phantom study with different reconstruction methods

PurposeTo compare the capability of CTs obtained with a silver or copper x-ray beam spectral modulation filter (Ag filter and Cu filter) and reconstructed with FBP, hybrid-type IR and deep learning reconstruction (DLR) for radiation dose reduction for lung nodule detection using a chest phantom study. Materials and methodsA chest CT phantom was scanned with a 320-detector row CT with Ag filter at 0.6, 1.6 and 2.5 mGy and Cu filters at 0.6, 1.6, 2.5 and 9.6 mGy, and reconstructed with the aforementioned methods. To compare image quality of all the CT data, SNRs and CNRs for any nodule were calculated for all protocols. To compare nodule detection capability among all protocols, the probability of detection of any nodule was assessed with a 5-point visual scoring system. Then, ROC analyses were performed to compare nodule detection capability of Ag and Cu filters for each radiation dose data with the same method and of the three methods for any radiation dose data and obtained with either filter. ResultsAt any of the doses, SNR, CNR and area under the curve for the Ag filter were significantly higher or larger than those for the Cu filter (p < 0.05). Moreover, with DLR, those values were significantly higher or larger than all the others for CTs obtained with any of the radiation doses and either filter (p < 0.05). ConclusionThe Ag filter and DLR can significantly improve image quality and nodule detection capability compared with the Cu filter and other reconstruction methods at each of radiation doses used.

Tunable dual color emission from the opposite faces of silicon nanoparticle embedded gel-glass

A luminescent silicon nanoparticle embedded gel-glass, prepared by room temperature hydrolysis and reduction of aminosilane, exhibits intriguing dual photoluminescence (PL) from opposite faces of the glass. The face, which is excited with UV, exhibits excitation energy dependent blue-green emission. As the excitation energy is varied from 350 nm to 450 nm the PL peaks shift from 435 nm to 506 nm. The opposite surface, on the other hand emits nearly excitation independent green light – the PL peak shifts by ∼17 nm as the excitation energy is varied from 350 nm to 450 nm. The luminescent properties provide interesting insights into the light emission mechanism from nanostructured silicon. Spectral filtering by reabsorption and photon reabsorption-reemission in a size distributed nanoparticle system having different optical gaps play a combined role in the observed dual emission. We show that the dual emission can be tuned by simply varying the thickness of the glass. Such dual emission renders the luminescent glass amenable for several applications as a novel solid state display material.

A switchable multi-wavelength mode-locked fiber laser based on a multi-mode interference device

A switchable multi-wavelength, passively mode-locked fiber laser in the 1 μm regime is demonstrated in this paper. The key principles of working include the nonlinear polarization rotation (NPR) mechanism for the mode-locked pulse generation and a single mode-multimode-single mode (SMS) structure as the spectral filter. The SMS structure is coiled on the paddles of a polarization controller (PC). Stable mode-locking operation is achieved at the fundamental repetition rate of 7.97 MHz. The pulse duration is 429 ps at the pump level of 216 mW, with the dual-wavelength laser emitting at 1037.86 nm and 1045.72 nm. By properly tuning the paddles of PCs, multi-wavelength mode-locked laser spectra can be observed switching from triple- to sextuple-wavelength, with the emission bandwidth of 27.6 nm from 1035.6 nm to 1063.2 nm.

Ultrafast Fiber Laser Emitting at 2.8 µm Based on a SESAM and a Broadband FBG

Ultrafast mid-infrared fiber lasers have been intensely studied in the last decade for the generation of high harmonics, molecular spectroscopy, material processing and remote sensing. Different designs have been investigated but most of them lacked the ease of use and reliability needed for their democratization. In this paper, we demonstrate a self-starting mode-locked mid-IR erbium-doped fiber laser based on a SESAM and a broadband uniform FBG that produces pulses as short as 15 ps. Different laser cavities were tested with varying FBG peak reflectance, spectral bandwidth and active fiber length. In addition, one cavity uses a pump combiner instead of injecting free-space the pump power through the fiber tip. The results of this study confirm that the FBG spectral bandwidth can efficiently control the duration of the almost Fourier-transform-limited pulses up to a limit seemingly dictated by the presence of water vapor in the laser cavity acting as narrow spectral filters. To a lower effect, the active fiber length influences the pulse duration. Finally, the use of an all-fiber pump combiner allows for a more compact and rugged design without altering the laser performances. This study represents a step towards the development of robust mid-infrared ultrafast all-fiber lasers.

Automatic lineaments detection using radar and optical data with an emphasis on geologic and tectonic implications: a case study of Kabul Block, eastern Afghanistan

The earth’s surface linear features, expressing geological lineaments, play a key role in identifying hydrothermal alteration and mineralization zones, as well as in understanding tectonic settings of a region. The objective of this investigation is to utilize a method for extracting lineaments automatically, which will be integrated and applied to identify geological-based lineaments by making use of remote sensing data. The study will further examine the structural pattern and temporal-spatial evolution of the lineaments and establish their connection with the primary active faults present in the Kabul Block. Multi-sensor data from radar (DEM (Digital Elevation Model)-5m, Sentinel-1B GRD (Ground Range Detected)) and optical sensors (Sentinel-2 MSI (Multispectral Instrument) and ASTER) were processed using spatial and spectral filters before automatic lineament extraction. LINE-module algorithm was applied to various illuminated hill-shades of DEM-5m, filtered HH (Horizonal-Horizonal) and VH (Vertical-Horizontal) of Sentinel-1GRD and PC1 (Principle Component) of Sentinel-2A MSI, and ASTER data to detect linear surface features. Extensive testing was conducted to verify the accuracy of extracted lineaments and to exclude any artificial lineaments in the study area. The radar and optical data results were compared while taking into consideration the geological and tectonic settings of the study area to select the most appropriate extracted lineaments. The DEM-5m and Sentinel-1B GRD showed the best result for identifying lineaments, and these were found to be highly correlated with previously available data in the Kabul Block. The final results of DEM-5m and Sentinel-1B GRD were further analyzed. The extracted lineaments were found to trend predominantly in a NW-SE and NE-SW directions, which is consistent with the results of other data. Temporal evolution and spatial distribution reveal that a high density of the lineaments is associated with Paleogene and Quaternary formations, while a low density is observed in Proterozoic, Paleozoic, and Mesozoic formations. The west and southwest edges of the Kabul Block are controlled by compressive stress trending NNW-SSE, while the north and southeast margins are influenced by NE-SW and ∼ N-S trending compressive stress.

Optical emission spectroscopy of an industrial thermal atmospheric pressure plasma jet: Parametric study of electron temperature

The plasma parameters of an industrial atmospheric pressure plasma jet (APPJ) are measured as a function of the electric power of the plasma generator, the gas flow, and the distance from the jet nozzle exit. The APPJ device operates an electric discharge under Argon gas flow resulting into a continuous Argon plasma jet in the kW power range expanding into ambient air. The plasma temperature Te and electron number density Ne are determined by optical emission spectroscopy (OES) analyzing up to 17 emission lines of neutral Ar (I) and singly-ionized Ar (II) species using the Saha-Boltzmann plot (SBP) method. For the accurate determination of plasma parameters a methodology is developed to overcome fundamental limitations in OES of Argon plasma that is due to the strongly different intensities of Ar (I) and Ar (II) emission lines. The most significant step is the use of spectral bandpass filters in the optical collection system. The experimental parameters of the jet device are playing a crucial role for APPJ applications (e.g. thermal load to the workpiece, plasma chemistry interaction etc.). We measure Te and Ne in dependence of the experimental parameters and obtain consistent and accurate values of Te = 9000 ± 150 K and Ne = 5 × 1015 cm−3 (typ.). For comparison, plasma characterization using only the neutral Ar lines gives inaccurate and inconsistent Te values. This highlights the importance of using also the ionic Ar lines. The developed methodology may be diversified and applied also for OES of other rare gas plasma.

On-chip parallel processing of quantum frequency comb

The frequency degree of freedom of optical photons has been recently explored for efficient quantum information processing. Significant reduction in hardware resources and enhancement of quantum functions can be expected by leveraging the large number of frequency modes. Here, we develope an integrated photonic platform for the generation and parallel processing of quantum frequency combs (QFCs). Cavity-enhanced parametric down-conversion with Sagnac configuration is implemented to generate QFCs with identical spectral distributions. On-chip quantum interference of different frequency modes is simultaneously realized with the same photonic circuit. High interference visibility is maintained across all frequency modes with the identical circuit setting. This enables the on-chip reconfiguration of QFCs. By deterministically separating QFCs without spectral filtering, we further demonstrate high-dimensional Hong-Ou-Mandel effect. Our work provides the critical step for the efficient implementation of quantum information processing with integrated photonics using the frequency degree of freedom.

Fourier-informed knot placement schemes for B-spline approximation

Fitting B-splines to scientific data is especially challenging when the given data contain noise, jumps, or corners. Here, we describe how periodic data sets with these features can be efficiently approximated with B-splines by analyzing the Fourier spectrum of the data. Our method uses a collection of spectral filters to compute high-order derivatives, smoothed versions of noisy data, and the locations of jump discontinuities. These quantities are then combined to choose knots that capture the qualitative features of the data, leading to accurate B-spline approximations with few knots. The method we introduce is direct and does not require any intermediate B-spline fitting before choosing the final knot distribution. Aside from fast Fourier transforms to transfer to and from Fourier space, the method runs in linear time with very little communication. We assess performance on several test cases in one and two dimensions, including data sets with jump discontinuities and noise. These tests show the method fits discontinuous data without spurious oscillations and remains effective in the presence of noise.

Formation and propagation mechanism of complex stacking fault in 180 μm thick 4H-SiC epitaxial layers

The formation mechanism and propagation behavior of a complex stacking fault (SF) in thick silicon carbide epitaxial layers was investigated using depth-resolved section X-ray topography and ultraviolet photoluminescence (UVPL) imaging. The SF is initiated in the substrate by deflection of a threading screw dislocation onto two basal half planes creating a Frank fault. This fault propagates into the epitaxial layer and interacts with several threading mixed dislocations, which creates both Frank and Shockley SFs on the same basal plane. UVPL images using different spectral filters show various SF type conversions occurring during propagation of the complex SF.

Flexible long-wave infrared snapshot multispectral imaging with a pixel-level spectral filter array.

This paper proposes and demonstrates a flexible long-wave infrared snapshot multispectral imaging system consisting of a simple re-imaging system and a pixel-level spectral filter array. A six-band multispectral image in the spectral range of 8-12 µm with full width at half maximum of about 0.7 µm each band is acquired in the experiment. The pixel-level multispectral filter array is placed at the primary imaging plane of the re-imaging system instead of directly encapsulated on the detector chip, which diminishes the complexity of pixel-level chip packaging. Furthermore, the proposed method possesses the merit of flexible functions switching between multispectral imaging and intensity imaging by plugging and unplugging the pixel-level spectral filter array. Our approach could be viable for various practical long-wave infrared detection applications.

Minimax design of two-channel critically sampled graph QMF banks

We address here the issue of designing high spectral selectivity graph filter banks, which require high-degree polynomial approximation. To achieve sharp transition band roll required for high-selectivity, the minimax criterion is used in the design formulation. A constraint optimization method is used for the design process, and a spectral transformation is employed to circumvent the numerical problems that arise with high-degree polynomials. Using the transformation, approximation problem can be recast as an approximation problem with trigonometric polynomials. The transformed constraint optimization problem can be solved using the readily available sequential quadratic programming (SQP) method. High order filters, with low maximum approximation error (MAE) and low maximum reconstruction error (MRE), can be designed with this method. An algorithm is also developed that allows the user to choose design parameters that will give the lowest possible MRE. The method can also be adapted to trade-off between reconstruction quality and spectral selectivity of the resulting filter bank. The suite of techniques presented allow the user to easily shape the spectral response of the filters, to suit the application at hand. Numerous examples, with comparisons, will be presented to demonstrate the versatility of the proposed techniques. Applications to nonlinear approximation and denoising of graph signal will also be considered.

Fiber laser with intracavity parallel Mach-Zehnder interferometers towards Vernier refractive index sensing.

In this paper, dual-wavelength laser emission of an erbium-doped fiber laser (EDFL) with a tunable distinct wavelength selection of the simultaneously produced laser lines was achieved by applying a parallel comb filter configuration based on the optical Vernier effect. The intracavity inserted proposed comb filter consists of two parallel branches to generate the Vernier effect. Each branch is an in-line Mach-Zehnder interferometer (MZI) filter, which is composed of a polarization-maintaining fiber fusion spliced between single-mode fibers with sphere shapes at both ends. The tunability of the selected laser wavelength was realized by submerging the proposed filter in different sodium chloride/water mixtures. The proposed comb filter-based Vernier effect was used to independently achieve the selection of the dual-wavelength EDFL lines and for refractive index (RI) sensing applications. The in-line M Z I 1 and M Z I 2 structures show a wavelength shift sensitivity to RI variations of -88 and 79nm/RIU, respectively. Our proposed MZI structure presents a reliable, straightforward, and low-cost spectral comb filter for separate tunable dual-wavelength laser generation in the c-band region. Furthermore, the proposed filter structure-based Vernier effect presents a new perspective and method in the RI sensing application.

Lensfree time-gated photoluminescent imaging

Fluorescence and, more generally, photoluminescence enable high contrast imaging of targeted regions of interest through the use of photoluminescent probes with high specificity for different targets. Fluorescence can be used for rare cell imaging; however, this often requires a high space-bandwidth product: simultaneous high resolution and large field of view. With bulky traditional microscopes, high space-bandwidth product images require time-consuming mechanical scanning and stitching. Lensfree imaging can compactly and cost-effectively achieve a high space-bandwidth product in a single image through computational reconstruction of images from diffraction patterns recorded over the full field of view of standard image sensors. Many methods of lensfree photoluminescent imaging exist, where the excitation light is filtered before the image sensor, often by placing spectral filters between the sample and sensor. However, the sample-to-sensor distance is one of the limiting factors on resolution in lensfree systems and so more competitive performance can be obtained if this distance is reduced. Here, we show a time-gated lensfree photoluminescent imaging system that can achieve a resolution of 8.77 µm. We use europium chelate fluorophores because of their long lifetime (642 µs) and trigger camera exposure ∼50 µs after excitation. Because the excitation light is filtered temporally, there is no need for physical filters, enabling reduced sample-to-sensor distances and higher resolutions.

Co-Existence of Watt-Level Dissipative Solitons and Synchronous Dual-Wavelength Mode-Locked Pulses in Yb Fiber Laser

We present a high-average-power laser configuration to simultaneously generate both dissipative solitons (DSs) and synchronous dual-wavelength mode-locked (ML) pulses using large-mode-area (LMA) fibers in combination with a birefringence-based chirped-pulse spectral filter. DSs with average power as high as 5.8 W and pulse width as short as 178 fs are obtained at the output port. Meanwhile, up to 1.5 W average power in synchronous dual-wavelength ML pulsed operation is also obtained at the rejected port with central wavelengths located at 1029.7 nm and 1046.1 nm respectively. To our knowledge, observation of such behavior with both DSs and synchronous dual-wavelength pulse generation simultaneously has not been yet realized in watt-level fiber lasers.

Spectral Filter Selection Based on Human Color Vision for Spectral Reflectance Recovery.

Spectral filters are an important part of a multispectral acquisition system, and the selection of suitable filters can improve the spectral recovery accuracy. In this paper, we propose an efficient human color vision-based method to recover spectral reflectance by the optimal filter selection. The original sensitivity curves of the filters are weighted using the LMS cone response function. The area enclosed by the weighted filter spectral sensitivity curves and the coordinate axis is calculated. The area is subtracted before weighting, and the three filters with the smallest reduction in the weighted area are used as the initial filters. The initial filters selected in this way are closest to the sensitivity function of the human visual system. After the three initial filters are combined with the remaining filters one by one, the filter sets are substituted into the spectral recovery model. The best filter sets under L-weighting, M-weighting, and S-weighting are selected according to the custom error score ranking. Finally, the optimal filter set is selected from the three optimal filter sets according to the custom error score ranking. The experimental results demonstrate that the proposed method outperforms existing methods in spectral and colorimetric accuracy, which also has good stability and robustness. This work will be useful for optimizing the spectral sensitivity of a multispectral acquisition system.

Reconstruction of Compressed Hyperspectral Image Using SqueezeNet Coupled Dense Attentional Net

This study addresses image denoising alongside the compression and reconstruction of hyperspectral images (HSIs) using deep learning techniques, since the research community is striving to produce effective results to utilize hyperspectral data. Here, the SqueezeNet architecture is trained with a Gaussian noise model to predict and discriminate noisy pixels of HSI to obtain a clean image as output. The denoised image is further processed by the tunable spectral filter (TSF), which is a dual-level prediction filter to produce a compressed image. Subsequently, the compressed image is analyzed through a dense attentional net (DAN) model for reconstruction by reverse dual-level prediction operation. All the proposed mechanisms are employed in Python and evaluated using a Ben-Gurion University-Interdisciplinary Computational Vision Laboratory (BGU-ICVL) dataset. The results of SqueezeNet architecture applied to the dataset produced the denoised output with a Peak Signal to Noise Ratio (PSNR) value of 45.43 dB. The TSF implemented to the denoised images provided compression with a Mean Square Error (MSE) value of 8.334. Subsequently, the DAN model executed and produced reconstructed images with a Structural Similarity Index Measure (SSIM) value of 0.9964 dB. The study proved that each stage of the proposed approach resulted in a quality output, and the developed model is more effective to further utilize the HSI. This model can be well utilized using HSI data for mineral exploration.