Turbidity analysis using visible and near-infrared light images

The conventional photoelectric detection system requires complex circuitry and spectroscopic systems as well as specialized personnel for its operation.To replace such a system,a method of measuring turbidity using a camera is proposed by combining the imaging characteristics of a digital camera and the high-speed information processing capability of a computer.Two turbidity measurement devices based on visible and near-infrared(NIR)light cameras and a light source driving circuit with constant light intensity were designed.The RGB data in the turbidity images were acquired using a self-developed image processing software and converted to the CIE Lab color space.Based on the relationship between the luminance,chromatic aberration,and turbidity,the turbidity detection models for luminance and chromatic aberration of visible and NIR light devices exhibiting values from 0-1000 NTU,less than 100 NTU,and more than 100 NTU were established.By comparing and analyzing the proposed models,the two measurement models with the best all-around performance were selected and fused to generate new measurement models.The experimental results prove that the correlation between the three models and the commercial turbidity meter measurements exhibite a significance value higher than 0.999.The error of the fusion model is within 1.05%,with a mean square error of 1.14.The visible light device has less error at low turbidity measurements and is less influenced by the color of the image.The NIR light device is more stable and accurate at full range and high turbidity measurements and is therefore more suitable for such measurements.

CAEFusion: A New Convolutional Autoencoder-Based Infrared and Visible Light Image Fusion Algorithm

To address the issues of incomplete information,blurred details,loss of details,and insufficient contrast in infrared and visible image fusion,an image fusion algorithm based on a convolutional autoencoder is proposed.The region attention module is meant to extract the background feature map based on the distinct properties of the background feature map and the detail feature map.A multi-scale convolution attention module is suggested to enhance the communication of feature information.At the same time,the feature transformation module is introduced to learn more robust feature representations,aiming to preserve the integrity of image information.This study uses three available datasets from TNO,FLIR,and NIR to perform thorough quantitative and qualitative trials with five additional algorithms.The methods are assessed based on four indicators:information entropy(EN),standard deviation(SD),spatial frequency(SF),and average gradient(AG).Object detection experiments were done on the M3FD dataset to further verify the algorithm’s performance in comparison with five other algorithms.The algorithm’s accuracy was evaluated using the mean average precision at a threshold of 0.5(mAP@0.5)index.Comprehensive experimental findings show that CAEFusion performs well in subjective visual and objective evaluation criteria and has promising potential in downstream object detection tasks.

Rapid determination of oil content of single peanut seed by near-infrared hyperspectral imaging

Oil content is a crucial indicator for evaluating the quality of peanuts.A rapid and non-destructive method to determine oil content of individual peanut seed can provide robust technical support for breeding high-oil-content peanut varieties.In this study,we established a rapid determination method using near-infrared hyperspectral imaging and chemometrics to assess the oil content of single peanut seed.After selecting key wavelengths through competitive adaptive reweighted sampling(CARS),uninformative variable elimination(UVE),and random frog(RF),we constructed an oil content calibration model based on partial least squares regression for single peanut seed.Validation results demonstrated that the correlation coefficient was 0.8393 with a root mean square error of 1.7771 in the calibration set,while it was 0.7915 with a root mean square error of 2.2943 in the independent prediction set.Most samples exhibited relative errors below 5%,confirming the reliability of this model in predicting oil content of single peanut seed.

I-DCGAN and TOPSIS-IFP:A simulation generation model for radiographic flaw detection images in light alloy castings and an algorithm for quality evaluation of generated images

The intelligent detection technology driven by X-ray images and deep learning represents the forefront of advanced techniques and development trends in flaw detection and automated evaluation of light alloy castings.However,the efficacy of deep learning models hinges upon a substantial abundance of flaw samples.The existing research on X-ray image augmentation for flaw detection suffers from shortcomings such as poor diversity of flaw samples and low reliability of quality evaluation.To this end,a novel approach was put forward,which involves the creation of the Interpolation-Deep Convolutional Generative Adversarial Network(I-DCGAN)for flaw detection image generation and a comprehensive evaluation algorithm named TOPSIS-IFP.I-DCGAN enables the generation of high-resolution,diverse simulated images with multiple appearances,achieving an improvement in sample diversity and quality while maintaining a relatively lower computational complexity.TOPSIS-IFP facilitates multi-dimensional quality evaluation,including aspects such as diversity,authenticity,image distribution difference,and image distortion degree.The results indicate that the X-ray radiographic images of magnesium and aluminum alloy castings achieve optimal performance when trained up to the 800th and 600th epochs,respectively.The TOPSIS-IFP value reaches 78.7%and 73.8%similarity to the ideal solution,respectively.Compared to single index evaluation,the TOPSIS-IFP algorithm achieves higher-quality simulated images at the optimal training epoch.This approach successfully mitigates the issue of unreliable quality associated with single index evaluation.The image generation and comprehensive quality evaluation method developed in this paper provides a novel approach for image augmentation in flaw recognition,holding significant importance for enhancing the robustness of subsequent flaw recognition networks.

Efficacy of multi-color near-infrared fluorescence with indocyanine green:A new imaging strategy and its early experience in laparoscopic cholecystectomy

BACKGROUND Near-infrared fluorescence imaging via using intravenous indocyanine green(ICG)has a wide range of applications in multiple surgical scenarios.In lapa-roscopic cholecystectomy(LC),it facilitates intraoperative identification of the biliary system and reduces the risk of bile duct injury.However,the usual single color fluorescence imaging(SCFI)has limitations in manifesting the fluorescence signal of the target structure when its intensity is relatively low.Moreover,sur-geons often experience visual fatigue.We hypothesized that a novel imaging stra-tegy,named multi-color fluorescence imaging(MCFI),could potentially address these issues by decreasing hepatic and background fluorescence pollution and improving biliary visualization.AIM To investigate the novel imaging strategy MCFI in LC.METHODS This was a single-center retrospective study conducted at Peking Union Medical College Hospital,Beijing,China.Patients who underwent LC from June 2022 to March 2023 by the same surgical team were enrolled.Demographic features,clinical and surgical information were collected.The clarity,visual comfort,and effectiveness of different imaging strategies were subjectively evaluated by surgeons.RESULTS A total of 155 patients were included,60 patients were in the non-ICG group in which only bright light illuminance without ICG was applied,60 patients were in the SCFI group,and 35 patients were in the MCFI group.No statist-ically significant differences were found in demographics or clinical history.Post-surgical complications were minimal in all 3 groups with no significant differences observed.MCFI improved the clarity of imaging and visual comfort.Clarity of imaging and visual comfort were improved with MCFI.CONCLUSION MCFI improves biliary visualization and reduces liver fluorescence contamination,which supports its routine use in LC.MCFI may also be a better choice than SCFI in other clinical settings.

Machine Learning-based Identification of Contaminated Images in Light Curve Data Preprocessing

Attitude is one of the crucial parameters for space objects and plays a vital role in collision prediction and debris removal.Analyzing light curves to determine attitude is the most commonly used method.In photometric observations,outliers may exist in the obtained light curves due to various reasons.Therefore,preprocessing is required to remove these outliers to obtain high quality light curves.Through statistical analysis,the reasons leading to outliers can be categorized into two main types:first,the brightness of the object significantly increases due to the passage of a star nearby,referred to as“stellar contamination,”and second,the brightness markedly decreases due to cloudy cover,referred to as“cloudy contamination.”The traditional approach of manually inspecting images for contamination is time-consuming and labor-intensive.However,we propose the utilization of machine learning methods as a substitute.Convolutional Neural Networks and SVMs are employed to identify cases of stellar contamination and cloudy contamination,achieving F1 scores of 1.00 and 0.98 on a test set,respectively.We also explore other machine learning methods such as ResNet-18 and Light Gradient Boosting Machine,then conduct comparative analyses of the results.

End-to-end computational design for an EUV solar corona multispectral imager with stray light suppression

An extreme ultraviolet solar corona multispectral imager can allow direct observation of high temperature coronal plasma,which is related to solar flares,coronal mass ejections and other significant coronal activities.This manuscript proposes a novel end-to-end computational design method for an extreme ultraviolet(EUV)solar corona multispectral imager operating at wavelengths near 100 nm,including a stray light suppression design and computational image recovery.To suppress the strong stray light from the solar disk,an outer opto-mechanical structure is designed to protect the imaging component of the system.Considering the low reflectivity(less than 70%)and strong-scattering(roughness)of existing extreme ultraviolet optical elements,the imaging component comprises only a primary mirror and a curved grating.A Lyot aperture is used to further suppress any residual stray light.Finally,a deep learning computational imaging method is used to correct the individual multi-wavelength images from the original recorded multi-slit data.In results and data,this can achieve a far-field angular resolution below 7",and spectral resolution below 0.05 nm.The field of view is±3 R_(☉)along the multi-slit moving direction,where R☉represents the radius of the solar disk.The ratio of the corona's stray light intensity to the solar center's irradiation intensity is less than 10-6 at the circle of 1.3 R_(☉).

Suppressing neuroinflammation using the near-infrared light emitted by (Sr,Ba)Ga_(12)O_(19): Cr^(3+) phosphor

Neurodegenerative diseases,such as Parkinson’s and Alzheimer’s diseases,affect the elderly worldwide and will become more prevalent as the global population ages.Neuroinflammation is a common characteristic of neurodegenerative diseases.By regulating the phenotypes of microglia,it is possible to suppress neuroinflammation and,in turn,help prevent neurodegenerative diseases.We report a noninvasive photonic approach to regulating microglia from overexcited M1/M2 to the resting M0 phenotype using a special near-infrared(NIR)light emitted by the SrGa_(12)O_(19)∶Cr^(3t) phosphor.The absorbance and internal and external quantum efficiencies of the optimal SreGa_(0.99)Cr_(0.01)_(12)O_(19) phosphor synthesized at 1400℃ for 8 h using 1%H_(3)BO_(3) t 1%AlF3 as flux are 53.9%,99.2%,and 53.5%;the output power and energyconversion efficiency of the LED device packaged using the optimal SrGa_(12)O_(19): Cr^(3+) phosphor driven at 20 mA reach unprecedentedly 19.69 mW and 37.58%,respectively.The broadband emission of the NIR LED device covers the absorption peaks of cytochrome c oxidase well,and the NIR light can efficiently promote the proliferation of microglia,produce adenosine triphosphate(ATP),reverse overexcitation,alleviate and inhibit inflammation,and improve cell survival rate and activity,showing great prospects for photomedicine application.

Fabrication of PdSe2/GaAs Heterojunction for Sensitive Near-Infrared Photovoltaic Detector and Image Sensor Application

In this study,we have developed a high-sensitivity,near-infrared photodetector based on PdSe2/GaAs heterojunction,which was made by transferring a multilayered PdSe2 film onto a planar GaAs.The as-fabricated PdSe2/GaAs heterojunction device exhibited obvious photovoltaic behavior to 808 nm illumination,indicating that the near-infrared photodetector can be used as a self-driven device without external power supply.Further device analysis showed that the hybrid heterojunction exhibited a high on/off ratio of 1.16×10^5 measured at 808 nm under zero bias voltage.The responsivity and specific detectivity of photodetector were estimated to be 171.34 mA/W and 2.36×10^11 Jones,respectively.Moreover,the device showed excellent stability and reliable repeatability.After 2 months,the photoelectric characteristics of the near-infrared photodetector hardly degrade in air,attributable to the good stability of the PdSe2.Finally,the PdSe2/GaAs-based heterojunction device can also function as a near-infrared light sensor.

MINIMIZING EXCITATION LIGHT LEAKAGE AND MAXIMIZING MEASUREMENT SENSITIVITY FOR MOLECULAR IMAGING WITH NEAR-INFRARED FLUORESCENCE

Near-infraredfluorescence(NIRF)imaging involves the separation of weakfluorescence signals from backscattered excitation light.The measurement sensitivity of current NIRF imaging systems is limited by the excitation light leakage through rejectionfilters.In this contribution,the authors demonstrate that the excitation light leakage can be suppressed upon using appropriatefilter combination and permutations.The excitation light leakage and measurement sensitivity were assessed and compared in this study by computing the transmission ratios of excitation to emission light collected and the signal-to-noise ratios in well-controlled phantom studies with differentfilter combinations and permutations.Using appropriatefilter combinations and permutations,we observe as much as two orders of magnitude reduction in the transmission ratio and higher signal-to-noise ratio.

Novel imaging system for positioning of the indocyanine green (ICG) target;visible projection of the near-infrared fluorescence image

Background: Even though NIR fluorescence imaging has many advantages in SLN mapping and cancer detection, NIR fluorescence imaging shows a serious drawback that NIR cannot be detected by the naked eye without any detectors. This limitation further disturbs accurate SLN detection and adequate tumor resection resulting in the presence of cancerous cells near the boundaries of surgically removed tissues. Materials and methods: To overcome the drawback of the conventional NIR imaging method, we suggest a novel NIR imaging system which can make the NIR fluorescence image visible to the naked eye as NIR fluorescence image detected by a video camera is processed by a computer and then projected back onto the NIR fluorescence excitation position with a projector using conspicuous color light. Image processing techniques were used for projection onto the exact position of the NIR fluorescence image. Also, we implemented a phantom experiment to evaluate the performance of the developed NIR fluorescence projection system by use of the ICG. Results: The developed NIR fluorescence projection system was applied in normal mouse model to confirm the usefulness of the system in the clinical field. A BALB/c nude mouse was prepared to be applied in normal mouse model and 0.25 mg/ml stock solution of the ICG was injected through a tail vein of the mouse. From the application in normal mouse model, we could confirm that the injected ICG stayed in the liver of the mouse and verify that the projection system projected the ICG fluorescence image at the exact location of the ICG by performing laparotomy of the mouse. Conclusions: From the application in normal mouse model, we could verify that the ICG fluorescence image was precisely projected back on the site where ICG fluorescence generated. It can be demonstrated that the NIR fluorescence projection system can make it possible to visualize the invisible NIR fluorescence image and to realize that SLN mapping and cancer detection in clinical surgery.

Estimating light interception using the color attributes of digital images of cotton canopies

Crop growth and yield depend on canopy light interception （LI）. To identify a low-cost and relatively efficient index for measuring LI, several color attributes of red-green-blue （RGB）, hue-saturation-intensity （HSI）, hue-saturation-value （HSV） color models and the component values of color attributes in the RGB color model were investigated using digital images at six cotton plant population densities in 2012-2014. The results showed that the LI values followed downward quadratic curves after planting. The red （R）, green （G） and blue （B） values varied greatly over the years, in accordance with Cai＇s research demonstrating that the RGB model is affected by outside light. Quadratic curves were fit to these color attributes at six plant population densities. Additionally, linear regressions of LI on every color attribute revealed that the hue （H） values in HSI and HSV were significantly linearly correlated with LI with a determination coefficient （R2）〉0.89 and a root mean square error （RMSE）=0.05. Thus, the H values in the HSI and HSV models could be used to measure LI, and this hypothesis was validated. The H values are new indexes for quantitatively estimating the LI of heterogeneous crop cano- pies, which will provide a theoretical basis for optimizing the crop canopy structure. However, further research should be conducted in other crops and under other growing and environmental conditions to verify this finding.

Fast recognition using convolutional neural network for the coal particle density range based on images captured under multiple light sources

A method based on multiple images captured under different light sources at different incident angles was developed to recognize the coal density range in this study.The innovation is that two new images were constructed based on images captured under four single light sources.Reconstruction image 1 was constructed by fusing greyscale versions of the original images into one image,and Reconstruction image2 was constructed based on the differences between the images captured under the different light sources.Subsequently,the four original images and two reconstructed images were input into the convolutional neural network AlexNet to recognize the density range in three cases:-1.5(clean coal) and+1.5 g/cm^(3)(non-clean coal);-1.8(non-gangue) and+1.8 g/cm^(3)(gangue);-1.5(clean coal),1.5-1.8(middlings),and+1.8 g/cm^(3)(gangue).The results show the following:(1) The reconstructed images,especially Reconstruction image 2,can effectively improve the recognition accuracy for the coal density range compared with images captured under single light source.(2) The recognition accuracies for gangue and non-gangue,clean coal and non-clean coal,and clean coal,middlings,and gangue reached88.44%,86.72% and 77.08%,respectively.(3) The recognition accuracy increases as the density moves further away from the boundary density.

Evaluation of COC183B2 antibody targeting ovarian cancer by near-infrared fluorescence imaging

Objective: To evaluate the imaging potential of a novel near-infrared(NIR) probe conjugated to COC183 B2 monoclonal antibodies(MAb) in ovarian cancer(OC).Methods: The expression of OC183 B2 antigen in OC was determined by immunohistochemical(IHC) staining using tissue microarrays with the H-score system and immunofluorescence(IF) staining of tumor cell lines.Imaging probes with the NIR fluorescent dye cyanine 7(Cy7) conjugated to COC183 B2 Mab were chemically engineered. OC183 B2-positive human OC cells(SKOV3-Luc) were injected subcutaneously into BALB/c nude mice. Bioluminescent imaging(BLI) was performed to detect tumor location and growth. COC183 B2-Cy7 at 1.1,3.3, 10, or 30 μg were used for in vivo fluorescence imaging, and phosphate-buffered saline(PBS), free Cy7 dye and mouse isotype immunoglobulin G(IgG)-Cy7(delivered at the same doses as COC183 B2-Cy7) were used as controls.Results: The expression of OC183 B2 with a high H-score was more prevalent in OC tissue than fallopian tube(FT) tissue. Among 417 OC patients, the expression of OC183 B2 was significantly correlated with the histological subtype, histological grade, residual tumor size, relapse state and survival status. IF staining demonstrated that COC183 B2 specifically expressed in SKOV3 cells but not HeLa cells. In vivo NIR fluorescence imaging indicated that COC183 B2-Cy7 was mainly distributed in the xenograft and liver with optimal tumor-to-background(T/B)ratios in the xenograft at 30 μg dose. The highest fluorescent signals in the tumor were observed at 96 h postinjection(hpi). Ex vivo fluorescence imaging revealed the fluorescent signals mainly from the tumor and liver. IHC analysis confirmed that xenografts were OC183 B2 positive.Conclusions: COC183 B2 is a good candidate for NIR fluorescence imaging and imaging-guided surgery in OC.

Advances in surgical techniques for gastric cancer:Indocyanine green and near-infrared fluorescence imaging.Is it ready for prime time?

Surgery is still the primary curative treatment for gastric cancer,which includes resection of the tumor with adequate margins and extended lymphadenectomy.In order to improve the operative results and the quality of life of patients,several endeavors have been made toward precision medicine through image-guided surgery,allowing access to real-time intraoperative anatomy and accurate tumor staging.The goal of the surgeon is to achieve a more precise,individualized,and less invasive surgery without compromising oncological efficiency and safety.In this perspective,we have demonstrated the role of indocyanine green(ICG)and near-infrared(NIR)fluorescence imaging method in gastric cancer surgery.This technique may be used to improve localization of the tumor,detection of sentinel lymph nodes(SLN),real-time lymphatic mapping,and blood flow assessment(anastomosis perfusion).

Stimulated Raman scattering microscopy with phase-controlled light focusing and aberration correction for rapid and label-free, volumetric deep tissue imaging

We report a novel stimulated Raman scattering(SRS)microscopy technique featuring phase-controlled light focusing and aberration corrections for rapid,deep tissue 3D chemical imaging with subcellular resolution.To accomplish phasecontrolled SRS(PC-SRS),we utilize a single spatial light modulator to electronically tune the axial positioning of both the shortened-length Bessel pump and the focused Gaussian Stokes beams,enabling z-scanning-free optical sectioning in the sample.By incorporating Zernike polynomials into the phase patterns,we simultaneously correct the system aberrations at two separate wavelengths(~240 nm difference),achieving a~3-fold enhancement in signal-to-noise ratio over the uncorrected imaging system.PC-SRS provides>2-fold improvement in imaging depth in various samples(e.g.,polystyrene bead phantoms,porcine brain tissue)as well as achieves SRS 3D imaging speed of~13 Hz per volume for real-time monitoring of Brownian motion of polymer beads in water,superior to conventional point-scanning SRS 3D imaging.We further utilize PC-SRS to observe the metabolic activities of the entire tumor liver in living zebrafish in cellsilent region,unraveling the upregulated metabolism in liver tumor compared to normal liver.This work shows that PCSRS provides unprecedented insights into morpho-chemistry,metabolic and dynamic functioning of live cells and tissue in real-time at the subcellular level.

Near-infrared lead chalcogenide quantum dots:Synthesis and applications in light emitting diodes

This paper reviews the recent progress in the synthesis of near-infrared(NIR) lead chalcogenide(PbX;PbX = PbS,PbSe, PbTe) quantum dots(QDs) and their applications in NIR QDs based light emitting diodes(NIR-QLEDs). It summarizes the strategies of how to synthesize high efficiency PbX QDs and how to realize high performance Pb X based NIR-QLEDs.

Deep learning-assisted common temperature measurement based on visible light imaging

Real-time,contact-free temperature monitoring of low to medium range(30℃-150℃)has been extensively used in industry and agriculture,which is usually realized by costly infrared temperature detection methods.This paper proposes an alternative approach of extracting temperature information in real time from the visible light images of the monitoring target using a convolutional neural network(CNN).A mean-square error of<1.119℃was reached in the temperature measurements of low to medium range using the CNN and the visible light images.Imaging angle and imaging distance do not affect the temperature detection using visible optical images by the CNN.Moreover,the CNN has a certain illuminance generalization ability capable of detection temperature information from the images which were collected under different illuminance and were not used for training.Compared to the conventional machine learning algorithms mentioned in the recent literatures,this real-time,contact-free temperature measurement approach that does not require any further image processing operations facilitates temperature monitoring applications in the industrial and civil fields.

SEGMENTATION ALGORITHM BASED ON EDGE-SEARCHING FOR MULTI-LINEAR STRUCTURED LIGHT IMAGES

Aiming at the problem that the existence of disturbances on the edges of light-stripe makes the segmentation of the light-stripes images difficult, a new segmentation algorithm based on edge-searching is presented. It firstly calculates every edge pixel＇s horizontal coordinate grads to produce the corresponding grads-edge, then uses a designed length-variable l D template to scan the light-stripes＇ grads-edges. The template is able to find the disturbances with different width utilizing the distributing character of the edge disturbances. The found disturbances are eliminated finally. The algorithm not only can smoothly segment the light-stripes images, but also eliminate most disturbances on the light-stripes＇ edges without damaging the light-stripes images＇ 3D information. A practical example of using the proposed algorithm is given in the end. It is proved that the efficiency of the algorithm has been improved obviously by comparison.

A Novel Sensing Imaging Equipment Under Extremely Dim Light for Blast Furnace Burden Surface:Starlight High-Temperature Industrial Endoscope

Blast furnace(BF)burden surface contains the most abundant,intuitive and credible smelting information and acquiring high-definition and high-brightness optical images of which is essential to realize precise material charging control,optimize gas flow distribution and improve ironmaking efficiency.It has been challengeable to obtain high-quality optical burden surface images under high-temperature,high-dust,and extremelydim(less than 0.001 Lux)environment.Based on a novel endoscopic sensing detection idea,a reverse telephoto structure starlight imaging system with large field of view and large aperture is designed.Combined with a water-air dual cooling intelligent self-maintenance protection device and the imaging system,a starlight high-temperature industrial endoscope is developed to obtain clear optical burden surface images stably under the harsh environment.Based on an endoscope imaging area model,a material flow trajectory model and a gas-dust coupling distribution model,an optimal installation position and posture configuration method for the endoscope is proposed,which maximizes the effective imaging area and ensures large-area,safe and stable imaging of the device in a confined space.Industrial experiments and applications indicate that the proposed method obtains clear and reliable large-area optical burden surface images and reveals new BF conditions,providing key data support for green iron smelting.