Liquid Crystal Tunable Filter Research Articles

Light consistency correction for the liquid crystal tunable filter hyperspectral imaging system

In hyperspectral images, every pixel encompasses continuous spectral information. Compared with traditional colorimeters, using hyperspectral imaging systems (HIS) for fabric color measurement can result in obtaining richer color information. However, measuring fabric colors with liquid crystal tunable filter HIS can lead to challenges related to light consistency. In this paper, we adopted an innovative approach, integrating gradient boosted decision trees with a sliding window algorithm to develop a uniformity calibration model addressing the illumination uniformity issue. To address the consistency issues across various light sources, we further adopted a deep neural network (DNN) model to correct the reflectance measurements under different light sources. Subsequently, this model was merged with the uniformity calibration model to form a light-consistency correction model. Through calibration, we successfully reduced the color difference of the corrected samples from 3.636 to 0.854, an enhancement of 76.51%. This means that after calibration we can achieve consistency in fabric color measurements under nonuniform lighting and different light sources.

Endoscopic Hyperspectral Imaging System to Discriminate Tissue Characteristics in Tissue Phantom and Orthotopic Mouse Pancreatic Tumor Model.

In this study, we developed an endoscopic hyperspectral imaging (eHSI) system and evaluated its performance in analyzing tissues within tissue phantoms and orthotopic mouse pancreatic tumor models. Our custom-built eHSI system incorporated a liquid crystal tunable filter. To assess its tissue discrimination capabilities, we acquired images of tissue phantoms, distinguishing between fat and muscle regions. The system underwent supervised training using labeled samples, and this classification model was then applied to other tissue phantom images for evaluation. In the tissue phantom experiment, the eHSI effectively differentiated muscle from fat and background tissues. The precision scores regarding fat tissue classification were 98.3% for the support vector machine, 97.7% for the neural network, and 96.0% with a light gradient-boosting machine algorithm, respectively. Furthermore, we applied the eHSI system to identify tumors within an orthotopic mouse pancreatic tumor model. The F-score of each pancreatic tumor-bearing model reached 73.1% for the KPC tumor model and 63.1% for the Pan02 tumor models. The refined imaging conditions and optimization of the fine-tuning of classification algorithms enhance the versatility and diagnostic efficacy of eHSI in biomedical applications.

A High Throughput Tunable Filter Module for Multispectral Imaging

AbstractTunable filters are vital not only for dynamic display but also in the miniaturized spectral imaging system. Yet, the present tunable devices possess low optical throughput, bulky volume, slow response, or complex configuration. In this paper, the study proposed and experimentally demonstrated a compact tunable filter module based on metagratings combined with liquid crystals (LC). By the synergetic effect between the birefringent LC and the dichroic metagratings, the module possesses distinct transmissive features by electrically tuning the LC material, resulting from the polarization interference effect. A simple nanoimprinting method, which is suitable for the mass production of nanostructures is adopted for low‐cost fabrication. These unique features endow the present tunable filter module with low cost, high optical throughput, and compact volume. As a proof of concept, a multispectral imager is built with this filter based on a commercial microscope. Experimental results show the system response is five times higher in optical throughput across the whole visible region than the traditional liquid crystal tunable filter and the signal‐to‐noise ratio can be improved by 7 dB. Considering the feasibility of nanoimprinting, this module paves the way for spectral imaging in low‐light conditions and on‐site scenarios such as tongue diagnosis and food quality inspection.

Гиперспектр буюу хэт олон сувагт “LCTF” камер ашиглан ургамлыг төрөл зүйлээр ангилан ялгах шинэ алгоритм

Within the framework of this research, a new algorithm was designed to determine the plant’s red edge slope and the Leaf Area Index (LAI) based on their spectral characteristics using a hyperspectral LCTF (Liquid Crystal Tunable Filter) camera. In the first week of July 2022, we took pictures of field vegetation in several fields in the Khar Yamaat Nature Reserve with the LCTF camera. From the collected data, the light reflectance spectrum of many species of growing plants was distinguished with the help of image processing, and with that, we created a spectrum dataset of 21 different species. From the generated dataset, we designed an algorithm to determine plant species or dry grass based on the red edge slope, which works with approximately 95% accuracy. The algorithm also calculates the leaf area index, and healthy plants are distinguished from damaged grass and non-plant species. The results of this work demonstrate that vegetation can be classified by vegetation’s red edge slope using hyperspectral cameras. In the future, it will be possible to determine the location and number of growing plants using the algorithm we developed on the images taken by modern satellites and drones.

Spectral control in the liquid crystal tunable filters

A liquid crystal is used as an electro-optical device with the advantages of low voltage driving, low power consumption, and thin flatness by utilizing its refractive index anisotropy (birefringence). In this research, a liquid crystal tunable filter (LCTF) with control of a main peak wavelength λ 0 and a full-width Δλ at half maximum is developed. The device contains two stacking liquid crystal layers with different wavelength dependences of birefringence Δn with their optical axes perpendicular to each other and controlling the light wavenumbers in a complementary manner (S-ECB).

High Light Efficiency Spectral Polarization Imaging Method Based on Mach–Zehnder Structured Liquid Crystal Tunable Filters and Variable Retarders

Liquid crystal tunable filters (LCTFs) are extensively used in hyperspectral imaging systems to obtain spectral information of target scenes. However, a typical LCTF can only filter linearly polarized light, greatly reducing the transmittance of the system and limiting its application in spectral and polarization imaging. In this paper, a spectropolarimeter using Mach–Zehnder structured LCTFs (MZ-LCTFs) combined with liquid crystal variable retarders (LCVRs) is proposed. The polarized beam splitter (PBS) can make full use of the two polarization components of the incident light to improve the transmittance of the system. Specifically, the results show that the mean pixel intensity (MPI) of spectral images is improved by 93.48% compared to a typical LCTF. Subsequently, the average signal to noise ratio (SNR) of filtered and unfiltered images when simultaneously using polarization S and P channels is increased by 2.59 dB compared to a single channel. In addition, the average Standard Deviations (STDs) of DoLP and DoCP are 0.016 and 0.018, respectively. The proposed method has the potential to be applied to obtain polarization information with high optical efficiency and a full spectrum in a wide band.

Hyperspectral imaging of lipids in biological tissues using near- and shortwave infrared transmission mode: A pilot study.

Label-free hyperspectral imaging (HSI) of lipids was demonstrated in the near-infrared (NIR) and shortwave infrared (SWIR) regions (950-1800 nm) using porcine tissue. HSI was performed in the transmission light-pass configuration, using a NIR-SWIR camera coupled with a liquid crystal tunable filter (LCTF). The transmittance spectra of the regions of interest (ROIs), which correspond to the lipid and muscle areas in the specimen, were utilized for the spectrum unmixing. The transmittance spectra in ROIs were compared with those recorded by a spectrophotometer using samples of adipose and muscle. The lipid optical absorption bands at 1210 and 1730 nm were first used for the unmixing and mapping. Then, we performed the continuous multiband unmixing over the entire available spectral range, thereby, considering a combination of characteristic absorption bands of lipids, proteins and water. The enhanced protocol demonstrates the best way of distinguishing small adipose inclusions of 1-10 μm size. This article is protected by copyright. All rights reserved.

A birefringent spectral demultiplexer enables fast hyper-spectral imaging of protoporphyrin IX during neurosurgery

Hyperspectral imaging and spectral analysis quantifies fluorophore concentration during fluorescence-guided surgery1–6. However, acquisition of the multiple wavelengths required to implement these methods can be time-consuming and hinder surgical workflow. To this end, a snapshot hyperspectral imaging system capable of acquiring 64 channels of spectral data simultaneously was developed for rapid hyperspectral imaging during neurosurgery. The system uses a birefringent spectral demultiplexer to split incoming light and redirect wavelengths to different sections of a large format microscope sensor. Its configuration achieves high optical throughput, accepts unpolarized input light and exceeds channel count of prior image-replicating imaging spectrometers by 4-fold. Tissue-simulating phantoms consisting of serial dilutions of the fluorescent agent characterize system linearity and sensitivity, and comparisons to performance of a liquid crystal tunable filter based hyperspectral imaging device are favorable. The new instrument showed comparable, if not improved, sensitivity at low fluorophore concentrations; yet, acquired wide-field images at more than 70-fold increase in frame rate. Image data acquired in the operating room during human brain tumor resection confirm these findings. The new device is an important advance in achieving real-time quantitative imaging of fluorophore concentration for guiding surgery.

Investigating the rate of turbidity impact on underwater spectral reflectance detection

Spectral reflectance detection of the targeted object is considered a vital inherent optical property for its potential to provide abundant spectral information, which is crucial in underwater spectral imaging. However, the coarse condition of the underwater environment due to turbidity causes extreme distortions in spectral reflectance detection due to the high absorption and scattering of light. To cope with the effects of light degradation on underwater spectral reflectance detection accuracy, the rate of the impacts of turbidity on spectral reflectance should be examined thoroughly. Therefore, we utilize a stare-type underwater spectral imaging system based on a liquid crystal tunable filter (LCTF) to study the effects of turbidity in underwater spectral imaging of various colored bodies. To examine the accuracy of underwater spectral reflectance detection based on escalating turbidity, the paper models the rate of increase in scattering intensity of the water body. Results show that, based on the non-linear increase in the pixel response of the black and white board, the rapid upsurge in scattering intensity occurs between 400nm to 500nm at different turbidity levels. Additionally, the spectral reconstruction of color bodies relative to the black and white board shows the maximum absolute deviation of 5.3% in spectral reflectance detection accuracy under varying turbidity. While employing underwater spectral imaging, the above findings of optimal band selection can find significant applications to improve the quality of underwater object detection.

Hyperspectral full polarization imaging system based on spatial modulation.

The hyperspectral full polarization imaging system (HFPIS) based on spatial modulation and liquid crystal tunable filter (LCTF) can modulate the polarization information of the target into the interferogram by means of interference imaging. It has the advantage of rapid imaging of the hyperspectral full polarization information from the target, and has good real time imaging and practicality. Through the spectroscopic imaging mode of a Savart prism, the corresponding interference optical path and imaging system are designed, including a beam expander, spatial modulator, LCTF, focusing system, and imaging sensor. This system can extract the different information from the target and demodulate it so as to obtain the hyperspectral polarization image. The experiment shows that the HFPIS can reveal the texture, contour, and other details of the target in the fog, and has obvious advantages over the traditional intensity imaging methods.

Study of multispectral polarization imaging in sea fog environment

Marine exploration has become a popular field of concern and research all over the world. The impact of sea fog on ocean exploration is very great, and how to carry out accurate identification of targets in the sea fog environment is a problem we urgently need to solve. In this paper, we simulated and analyzed the particle distribution characteristics of the sea fog layer by using the principle of Mie scattering, designed a spectral polarization imaging system by using Liquid Crystal Variable Retarder (LCVR) and Liquid Crystal Tunable Filter (LCTF) according to the principle of spectral spectroscopy and polarization imaging, conducted calibration experiments on liquid crystal tunable filter, and carried out experiments on visibility, wavelength and imaging distance that affect the experimental results of polarization imaging of sea fog environment. The experimental results show that the polarization decreases with the increase of imaging distance; in the typical visibility (5 km for light fog, 0.5 km for medium fog and 0.05 km for dense fog), the higher the visibility, the higher the polarization; for the typical wavelengths of visible light (450 nm, 530 nm and 670 nm), the polarization increase with the increase of wavelength.

Study of a deep learning-based method for improving the spectral resolution of the spectral scanning hyperspectral imaging system via synthetic spectral image data

Hyperspectral imaging (HSI) techniques, measuring spatial and spectral information, have shown the ability to identify targets based on their spectral features. Among many HSI methods, a spectral scanning HSI method implemented using a tunable filter has been widely used in various applications due to wide-area HSI capability and cost-effectiveness. However, the limitation of the spectral scanning method is poor spectral resolution compared to other spectral imaging methods using dispersive materials. To overcome this limitation, we exploited a recently developed deep-learning model that retrieves multispectral information from an red, green, and blue image. Moreover, this study proposed that a color chart consisting of 18 colors could be a standard target for training the deep-learning model under various spectral scanning HSI conditions. The simulation work was performed to demonstrate the feasibility of the proposed method using synthetic hyperspectral images. Realistic synthetic data was prepared using spectral data obtained via a spectrometer (ground-truth data) and artificial filters emulating a liquid-crystal tunable filter. We found that the deep-learning model trained via a supervised learning approach using synthetic hyperspectral images successfully retrieved high-resolution spectral data. In addition, the trained deep-learning model retrieved robust spectral profiles of random colors which were not used in the training process. Collectively, the deep learning-based spectral scanning method could improve the spectral resolution of the imaging system, and the color chart would be a good and practical standard training target for the deep learning model.

An ultrathin fiber-based fluorescent imaging probe based on hyperspectral imaging

Most fluorescent imaging systems use pre-defined filter sets with limited flexibility in the choice of excitation and emission bands and suffer from crosstalk between different fluorophores for multiplexed imaging. When implemented in the context of optical fibers for biological imaging in a remote setting with space constraints, the size of the fiber probe is crucial. To overcome these challenges, we combined the merits of hyperspectral imaging and an ultrathin optical imaging fiber where fluorescent images at 311 spectral bands in the visible range were captured using a liquid crystal tunable filter with a fiber probe of 500 μm in diameter. Fluorescent experiments were performed on quantum dots mixtures and genetically modified E. coli bacteria mixtures to demonstrate the system’s capabilities for multiplexed imaging. Images were first processed using a Fourier transform filtering technique to remove the fiber core pattern artifact. Subsequently, the classification of different types of bacteria samples was calculated using two methods, namely, spectral unmixing with maximum abundance and spectral matching with minimum spectral angle distance. Finally, the spatial distributions of individual bacteria types were overlaid with the mixture image, and the two classification results matched well. A simple-to-use graphic user interface (GUI) platform for the hyperspectral imaging fiber probe system was developed which performs image processing and displays the classification results. The methods and results presented will appeal to the optical fiber-based imaging and hyperspectral imaging communities in general and have great potential for biological imaging applications.

Deep learning reconstruction enables full-Stokes single compression in polarized hyperspectral imaging

Polarized hyperspectral imaging, which has been widely studied worldwide, can obtain four-dimensional data including polarization, spectral, and spatial domains. To simplify data acquisition, compressive sensing theory is utilized in each domain. The polarization information represented by the four Stokes parameters currently requires at least two compressions. This work achieves full-Stokes single compression by introducing deep learning reconstruction. The four Stokes parameters are modulated by a quarter-wave plate (QWP) and a liquid crystal tunable filter (LCTF) and then compressed into a single light intensity detected by a complementary metal oxide semiconductor (CMOS). Data processing involves model training and polarization reconstruction. The reconstruction model is trained by feeding the known Stokes parameters and their single compressions into a deep learning framework. Unknown Stokes parameters can be reconstructed from a single compression using the trained model. Benefiting from the acquisition simplicity and reconstruction efficiency, this work well facilitates the development and application of polarized hyperspectral imaging.

Optimal design of Lyot liquid crystal tunable filter based on the artificial immune algorithm

Optimal design of Lyot liquid crystal tunable filter based on the artificial immune algorithm

A Tunable Hyperspectral Imager for Detection and Quantification of Marine Biofouling on Coated Surfaces

Fouling control coatings (FCCs) are used to prevent the accumulation of marine biofouling on, e.g., ship hulls, which causes increased fuel consumption and the global spread of non-indigenous species. The standards for performance evaluations of FCCs rely on visual inspections, which induce a degree of subjectivity. The use of RGB images for objective evaluations has already received interest from several authors, but the limited acquired information restricts detailed analyses class-wise. This study demonstrates that hyperspectral imaging (HSI) expands the specificity of biofouling assessments of FCCs by capturing distinguishing spectral features. We developed a staring-type hyperspectral imager using a liquid crystal tunable filter as the wavelength selective element. A novel light-emitting diode illumination system with high and uniform irradiance was designed to compensate for the low-filter transmittance. A spectral library was created from reflectance-calibrated optical signatures of representative biofouling species and coated panels. We trained a neural network on the annotated library to assign a class to each pixel. The model was evaluated on an artificially generated target, and global accuracy of 95% was estimated. The classifier was tested on coated panels (exposed at the CoaST Maritime Test Centre) with visible intergrown biofouling. The segmentation results were used to determine the coverage percentage per class. Although a detailed taxonomic description might be complex due to spectral similarities among groups, these results demonstrate the feasibility of HSI for repeatable and quantifiable biofouling detection on coated surfaces.

Pulse fluorescence LIDAR system for identification and low concentration measurements of Phaeocystis globosa cells and colonies

A pulse fluorescent LIDAR system (pfLIDAR) for early-warning of Phaeocystis globosa cells and colonies outbreak is developed and demonstrated, which can realize species identification and low concentration measurement of Phaeocystis globosa in water areas. The system utilizes a high-power 532 nm nanosecond pulsed laser as excitation light source and a photomultiplier tube combined with a liquid crystal tunable filter as the detection setup. Measurement of Phaeocystis globosa solitary cells and colonies were performed in a water tank, simulating microalgae distributions and concentrations prior to blooms in real water areas. The concentration of Phaeocystis globosa cells or colonies can be accurately measured based on the received fluorescent echo signals. Obtained echo signals combined with spectral information can be used for species identification of high accuracy using a PCA-SVM method. The system works in staring scanning mode, and a gating technique is used to filter out scattering and background fluorescence noise in water path, which improves the SNR of detected fluorescent echo signals. Experimental demonstration results demonstrate the capability of the system for Phaeocystis globosa identification and biomass assessment, indicating a significant potential of the system for detailed monitoring in aquatic ecosystems.

5D-fusion imaging for surface shape, polarization, and hyperspectral measurement.

We present a five-dimensional (5D) imager that is capable of simultaneous detection of the surface shape, spectral characteristics, and polarization states of macroscopic objects, and straightforwardly fuse collected data into a 5D data set. A polarized module that uses a polarized camera obtains polarized images, while a 3D hyperspectral module reconstructs the target as a 3D point cloud using a fringe projection technique. A liquid-crystal tunable filter is placed in front of the camera of this module to acquire spectral data that can be assigned to corresponding point clouds directly. The two modules are coupled by a dual-path configuration that allows the polarization information to be merged into a comprehensive point cloud with spectral information, generating a new 5D model. The 5D imager shows excellent performance, with a spectral resolution of 10 nm, depth accuracy of 30.7 µm, and imaging time of 8 s. Sample experiments on a toy car with micro scratch defects and a yellowing plant are presented to demonstrate the capabilities of the 5D imager and its potential for use in a broad range of applications, such as industrial manufacturing inspection, plant health monitoring, and biological analysis.

Compressive full-Stokes polarization and flexible hyperspectral imaging with efficient reconstruction

Polarization hyperspectral compressive imaging has wide application prospects due to reflecting rich information of the target from the spatial, spectral and polarization dimensions. However, compressive imaging methods are limited in system complexity and reconstruction efficiency. In this paper, the combination of a quarter-wave plate, a linear polarizer and a liquid crystal tunable filter enables compressive full-Stokes polarization and flexible hyperspectral imaging. Then, the four Stokes parameters can be quickly reconstructed with high precision through numerical operations and compressive sensing. Both simulations and experiments verify the effectiveness of this method. Compared with the feature scaling method, this work shortens the reconstruction time by 83.73%, and greatly improves the reconstruction result of the first Stokes parameter from limited reconstruction to accurate solution. This work not only gives insights from efficient reconstruction to system design, but also creates conditions for the application of polarization hyperspectral imaging in specific fields.

(Invited) Hyperspectral Detection of the Fluorescence Shift between Enantiomers of Empty and Water-Filled Single-Wall Carbon Nanotubes

The unusually chiral-structure-dependent properties of single-wall carbon nanotubes (SWCNTs) are also strongly influenced by their internal and external environment, which can therefore be investigated through optical spectroscopy.[1,2] In order to study this influence in detail, we developed a hyperspectral IR fluorescence imaging setup based on a microscope with a liquid crystal tunable filter. By resolving the spectra of individual SWCNTs, and even along the length of SWCNTs, the effect of inhomogeneous broadening is largely eliminated, and spectral details can be resolved which are inaccessible in bulk spectroscopy. An automated image processing scheme is used to obtain statistics on large numbers of individual SWCNTs. In particular, we show that not only the spectral shift in emission between empty and water-filled[1] chirality sorted SWCNTs can be resolved, but even separate emission peaks are observed for the left- and right-handed enantiomers, which interact slightly differently with the chiral surfactant with which they are coated. The approach is particularly promising for the quantification of enantio-selective separation results.[1] W. Wenseleers et al., Adv. Mater. 19, 2274 (2007); S. Cambré and W. Wenseleers, Angew. Chem. 50, 2764 (2011); S. Cambré et al., ACS Nano 6, 2649 (2012). [2] J. Campo et al., ACS Nano 2021, 15, 2301−2317.