Image Gray Value Research Articles

Enhancing Cross-Modal Camera Image and LiDAR Data Registration Using Feature-Based Matching

Registering light detection and ranging (LiDAR) data with optical camera images enhances spatial awareness in autonomous driving, robotics, and geographic information systems. The current challenges in this field involve aligning 2D-3D data acquired from sources with distinct coordinate systems, orientations, and resolutions. This paper introduces a new pipeline for camera–LiDAR post-registration to produce colorized point clouds. Utilizing deep learning-based matching between 2D spherical projection LiDAR feature layers and camera images, we can map 3D LiDAR coordinates to image grey values. Various LiDAR feature layers, including intensity, bearing angle, depth, and different weighted combinations, are used to find correspondence with camera images utilizing state-of-the-art deep learning matching algorithms, i.e., SuperGlue and LoFTR. Registration is achieved using collinearity equations and RANSAC to remove false matches. The pipeline’s accuracy is tested using survey-grade terrestrial datasets from the TX5 scanner, as well as datasets from a custom-made, low-cost mobile mapping system (MMS) named Simultaneous Localization And Mapping Multi-sensor roBOT (SLAMM-BOT) across diverse scenes, in which both outperformed their baseline solutions. SuperGlue performed best in high-feature scenes, whereas LoFTR performed best in low-feature or sparse data scenes. The LiDAR intensity layer had the strongest matches, but combining feature layers improved matching and reduced errors.

Optimizing Voltage for Effective X-ray Computed Tomography Scan: A Study on Varied Soil Bulk Densities and Container Sizes

Numerous studies have highlighted the role of X-ray computed tomography (X-ray CT) in understanding root architecture. Nevertheless, setting definitive scanning parameters for diverse soils in varied container sizes remains challenging. This study investigates the influence of X-ray CT system voltage on the penetration capability in diverse soils and container sizes, focusing on two key parameters: (1) gray values, which indicate X-ray attenuation and contribute to image contrast, and (2) signal-to-noise ratio, a measure of image clarity. Five soil samples were collected from various depths within a soil profile to encompass bulk density values ranging from 1.34 to 1.84 g·cm−3 to conduct the experiment. Containers with dimensions of 6 × 6 × 6 cm³, 8 × 8 × 6 cm³, 10 × 10 × 6 cm³, 12 × 12 × 6 cm³, 14 × 14 × 6 cm³, and 16 × 16 × 6 cm³ were used. Voltage levels spanning 75 to 225 kV, in 25-kV increments, were applied to each sample. The observed gray values of the X-ray images were fitted using a logistic model of three parameters. Results showed that increasing voltage leads to enhanced penetration up to a plateau point, irrespective of soil density or container size. This plateau could potentially yield higher quality scans, given that lower voltages result in subdued gray values and reduced image contrast. Notably, it was observed that soil properties, including mineral composition, directly affect image gray values. This study established optimal voltage settings for specific soil types at fixed densities, offering valuable insights for researchers investigating soil–root interactions. Although the current findings are based on five soils, a more extensive sampling encompassing diverse soil textures and densities is necessary for a comprehensive understanding of X-ray penetration behavior across various soil types.

Programming of a Portable Digital Monitoring System-Integrated DNA Aptamer Reversely Regulated Oxidase-Like Nanozyme for Real-Time Dynamic Analysis of Atmospheric Perfluorooctanoic Acid.

Timely and efficient analysis of the fluorinated per- and polyfluoroalkyl substances (PFAS) in an atmospheric environment is critical to environmental pollution traceability, early warnings, and governance. Here, a portable, reliable, and intelligent digital monitoring device for onsite real-time dynamic analysis of atmospheric perfluorooctanoic acid (PFOA) is proposed. The sensing mechanism is attributed to the oxidase-like activity of PtCoNPs@g-C3N4 that is reversely regulated by the surface modification of a PFOA-recognizable DNA aptamer, engineering a PFOA-activated oxidase-like activity of nanozyme (Apt-PtCoNPs@g-C3N4) to combine the nonfluorescence o-phenylenediamine (OPD) as the dual-modality response system. The present PFOA interacts with its DNA aptamer and dissociates from the surface of Apt-PtCoNPs@g-C3N4, restoring the oxidase-like activity of PtCoNPs@g-C3N4 to oxidize OPD into yellow fluorescence 2,3-diphenylaniline (DAP), thereby observing a PFOA-triggered colorimetric as well as fluorescence dual-modality change. Then, a hydrogel kit-programmed Apt-PtCoNPs@g-C3N4 + OPD system is used as the sensitive element to incorporate into this homemade portable device, automatically gathering and processing the PFOA-triggered hydrogel colorimetric and fluorescence image gray values by our self-weaving software, ultimately realizing the onsite real-time dynamic analysis of atmospheric PFOA surrounding a fluorochemical production plant. This work provides a direction and theoretical foundation for designing portable onsite screening devices that cater to other atmospheric contaminants detection requirements.

Exploring the Potential of a Novel Iodine-Based Material as an Alternative Contrast Agent in X-ray Imaging Studies.

Contrast-enhanced mammography is one of the new emerging imaging techniques used for detecting breast tissue lesions. Optimization of imaging protocols and reconstruction techniques for this modality, however, requires the involvement of physical phantoms. Their development is related to the use of radiocontrast agents. This study assesses the X-ray properties of a novel contrast material in clinical settings. This material is intended for experimental use with physical phantoms, offering an alternative to commonly available radiocontrast agents. The water-soluble sodium salt of the newly synthesized diiodine-substituted natural eudesmic acid, Sodium 2,6-DiIodo-3,4,5-TriMethoxyBenzoate [NaDITMB], has been investigated with respect to one of the most commonly applied radiocontrast medium in medical practice-Omnipaque®. For this purpose, simulation and experimental studies were carried out with a computational phantom and a physical counterpart, respectively. Synthetic and experimental X-ray images were subsequently produced under varying beam kilovoltage peaks (kVps), and the proposed contrast material was evaluated. Simulation results revealed equivalent absorptions between the two simulated radiocontrast agents. Experimental findings supported these simulations, showing a maximum deviation of 3.7% between the image gray values of contrast materials for NaDITMB and Omnipaque solutions for a 46 kVp X-ray beam. Higher kVp X-ray beams show even smaller deviations in the mean grey values of the imaged contrast agents, with the NaDITMB solution demonstrating less than a 2% deviation compared to Omnipaque. The proposed contrast agent is a suitable candidate for use in experimental work related to contrast-enhanced imaging by utilizing phantoms. It boasts the advantages of easy synthesis and is recognized for its safety, ensuring a secure environment for both the experimenter and the environment.

A Degraded Finger Vein Image Recovery and Enhancement Algorithm Based on Atmospheric Scattering Theory.

With the development of biometric identification technology, finger vein identification has received more and more widespread attention for its security, efficiency, and stability. However, because of the performance of the current standard finger vein image acquisition device and the complex internal organization of the finger, the acquired images are often heavily degraded and have lost their texture characteristics. This makes the topology of the finger veins inconspicuous or even difficult to distinguish, greatly affecting the identification accuracy. Therefore, this paper proposes a finger vein image recovery and enhancement algorithm using atmospheric scattering theory. Firstly, to normalize the local over-bright and over-dark regions of finger vein images within a certain threshold, the Gamma transform method is improved in this paper to correct and measure the gray value of a given image. Then, we reconstruct the image based on atmospheric scattering theory and design a pixel mutation filter to segment the venous and non-venous contact zones. Finally, the degraded finger vein images are recovered and enhanced by global image gray value normalization. Experiments on SDUMLA-HMT and ZJ-UVM datasets show that our proposed method effectively achieves the recovery and enhancement of degraded finger vein images. The image restoration and enhancement algorithm proposed in this paper performs well in finger vein recognition using traditional methods, machine learning, and deep learning. The recognition accuracy of the processed image is improved by more than 10% compared to the original image.

Thermal and Mechanical Properties of Plain Woven Ceramic Matrix Composites by the Imaged-Based Mesoscopic Model

The mesoscopic finite element model of ceramic matrix composites is developed by reconstructing computed tomography images. The relationship between microstructure and macroscopic properties and their dispersion are explored. Firstly, a three-dimensional reconstructed computed tomography model was carried out to calculate the orientation of the components. Secondly, the orientation and gray value of images were used to identify the basic structure of composites including fiber tows, matrix, and porosities. Finally, the two-dimensional finite element model was developed to analyze the macroscopic thermal and mechanical properties. The numerical results showed that: (1) the geometries and distributions of pores had a significant effect on the through-thickness modulus and thermal conductivity; (2) the stress and heat flow concentration area on the material surface usually occurred in narrow gaps between closely spaced longitudinal tows; (3) the elastic modulus and thermal conductivity were dispersive, and their distribution followed by two-parameter Weibull distribution.

Magnetic nanoparticle loaded biodegradable vascular stents for magnetic resonance imaging and long-term visualization.

Benefiting from their good biosafety and bioabsorbability, polymeric biodegradable stents (BDSs) have promising application prospects in the treatment of cardiovascular diseases. However, due to the low density of the polymer itself, it is difficult to visualize with medical imaging techniques such as CT and MRI, which leads to difficulties in accurate BDS localization and subsequent non-invasive evaluation. Therefore, modification of BDSs to adapt to monitoring techniques for clinical use without affecting their biocompatibility and mechanical properties is a promising strategy to support the clinical translation of BDSs. In this study, Fe3O4 superparamagnetic iron oxide nanoparticles (SPIONs) were synthesized to modify the BDS by ultrasonic spraying. Due to the T2-weighted MR imaging enhancement capability of SPIONs, the fabricated SPION-BDS can be entirely visualized and long-term monitored under MR imaging. Further, a stent degradation assessment method based on the analysis of image gray value changes was established. In conclusion, the constructed SPION-BDS provides a possible solution for precise localization of BDSs after implantation, and furthermore, opens up opportunities for long-term non-invasive monitoring of in vivo BDS degradation and multimodal imaging assessment of vascular endothelial remodeling.

A Benchmark for the Evaluation of Corner Detectors

Corners are an important kind of image feature and play a crucial role in solving various tasks. Over the past few decades, a great number of corner detectors have been proposed. However, there is no benchmark dataset with labeled ground-truth corners and unified metrics to evaluate their corner detection performance. In this paper, we build three benchmark datasets for corner detection. The first two consist of those binary and gray-value images that have been commonly used in previous corner detection studies. The third one contains a set of urban images, called the Urban-Corner dataset. For each test image in these three datasets, the ground-truth corners are manually labeled as objectively as possible with the assistance of a line segment detector. Then, a set of benchmark evaluation metrics is suggested, including five conventional ones: the precision, the recall, the arithmetic mean of precision and recall (APR), the F score, the localization error (Le), and a new one proposed in this work called the repeatability referenced to ground truth (RGT). Finally, a comprehensive evaluation of current state-of-the-art corner detectors is conducted.

The monitoring of bridge under complex illumination based on digital image technology

In the actual bridge monitoring field, the change of illumination is an urgent problem to be solved in digital image technology. At present, when using markers to measure the displacement of bridges, problems such as the change of image gray value, uneven distribution of light, and surface defects of material cause identification difficulties. In this paper, a new recognition algorithm called the dual-channel method is proposed. Based on the dark channel principle and OSTU theory, the dual-channel method can automatically select the image gray threshold, which is used to solve the recognition problem to achieve long-term monitoring. Experimental results show this method can accurately identify the markers under complex lighting conditions and greatly expand the range of gray thresholds. Compared with the traditional displacement meter, measurement errors of the dual-channel method are less than 3%. The new method can achieve real-term effective bridge monitoring, which provides a theoretical basis for practical engineering.

Portable and low-cost hologram verification module using a snapshot-based hyperspectral imaging algorithm

One of the challenges in differentiating a duplicate hologram from an original one is reflectivity. A slight change in lighting condition will completely change the reflection pattern exhibited by a hologram, and consequently, a standardized duplicate hologram detector has not yet been created. In this study, a portable and low-cost snapshot hyperspectral imaging (HSI) algorithm-based housing module for differentiating between original and duplicate holograms was proposed. The module consisted of a Raspberry Pi 4 processor, a Raspberry Pi camera, a display, and a light-emitting diode lighting system with a dimmer. A visible HSI algorithm that could convert an RGB image captured by the Raspberry Pi camera into a hyperspectral image was established. A specific region of interest was selected from the spectral image and mean gray value (MGV) and reflectivity were measured. Results suggested that shorter wavelengths are the most suitable for differentiating holograms when using MGV as the parameter for classification, while longer wavelengths are the most suitable when using reflectivity. The key features of this design include low cost, simplicity, lack of moving parts, and no requirement for an additional decoding key.

Segmentation for Human Motion Injury Ultrasound Medical Images Using Deep Feature Fusion

Image processing technology assists physicians in the analysis of athletes’ human motion injuries, not only to improve the accuracy of athletes’ injury detection but also to improve the localization and recognition of injury locations. It is important to accurately segment human motion injury ultrasound medical images. To address many problems such as poor effect of traditional ultrasonic medical image segmentation algorithm for a sports injury. Therefore, we propose a segmentation algorithm for human motion injury ultrasound medical images using deep feature fusion. First, the accurate estimated value of human posture is extracted and combined with image texture features and image gray value as the target feature value of the ultrasonic medical image of human motion injury. Second, the image features are deeply fused by an adaptive fusion algorithm to enhance the image resolution. Finally, the best segmentation value of the image is obtained by the trained support vector machine to realize the accurate segmentation of human motion injury ultrasonic medical image. The results show that the average accuracy of the posture accurate estimation of the proposed algorithm is 95.97%; the segmentation time of the human motion injury ultrasound medical image of the proposed algorithm is below 150 ms; and the convergence of the algorithm is completed when the number of iterations is 3. The maximum segmentation error rate is 2.68%. The image segmentation effect is consistent with the ideal target segmentation effect. The proposed algorithm has important application value in the field of ultrasonic medical diagnosis of sports injury.

Effect of Gray Value Discretization and Image Filtration on Texture Features of the Pancreas Derived from Magnetic Resonance Imaging at 3T.

Radiomics of pancreas magnetic resonance (MR) images is positioned well to play an important role in the management of diseases characterized by diffuse involvement of the pancreas. The effect of image pre-processing configurations on these images has been sparsely investigated. Fifteen individuals with definite chronic pancreatitis (an exemplar diffuse disease of the pancreas) and 15 healthy individuals were included in this age- and sex-matched case-control study. MR images of the pancreas were acquired using a single 3T scanner. A total of 93 first-order and second-order texture features of the pancreas were compared between the study groups, by subjecting MR images of the pancreas to 7 image pre-processing configurations related to gray level discretization and image filtration. The studied parameters of intensity discretization did not vary in terms of their effect on the number of significant first-order texture features. The number of statistically significant first-order texture features varied after filtering (7 with the use of logarithm filter and 3 with the use of Laplacian of Gaussian filter with 5 mm σ). Intensity discretization generally affected the number of significant second-order texture features more markedly than filtering. The use of fixed bin number of 16 yielded 42 significant second-order texture features, fixed bin number of 128–38 features, fixed bin width of 6–24 features, and fixed bin width of 42–26 features. The specific parameters of filtration and intensity discretization had differing effects on radiomics signature of the pancreas. Relative discretization with fixed bin number of 16 and use of logarithm filter hold promise as pre-processing configurations of choice in future radiomics studies in diffuse diseases of the pancreas.

Illustration Art Based on Visual Communication in Digital Context

With the rapid advancement of digital media technology in recent years, a digital context has emerged and developed under its influence. Visual communication relies heavily on illustration art. It is widely used in many fields of modern design because it is an important form of visual communication. We propose a visual communication design method for small and weak target images based on temporal and spatial filtering. We calculate the probability of gray pixels appearing using the histogram equalization method, assign thresholds to pixels, and enhance the gray value of images using the equalization process. We analyze the characteristics of each element of the 3D video system and the appropriate visual communication design form after performing calculations and combining them with the basic theory of visual communication design. Create system test links, test indicators, and test results. It has been demonstrated that the restored image integrity of the system is higher than that of the existing system, and that its performance is better.

Investigation on Hydration and Deformation Characteristics of Shale Using X-ray Computed Tomography

The physical and mechanical properties of shale are significant for completion engineering and fracturing design for shale oil and gas production. To further study the water effect and the deformation characteristics of shale, some cylindrical specimens were soaked at three periods (0, 20, and 60 days), the in situ uniaxial compression tests were conducted, and the CT technique was used at different hydration and loading stages. Based on the CT results, the image gray variance value in each hydration specimen was extracted; the evolution results show that the water imbibition has a significant influence on the structure of shale in the early soaking stage, and the influence degree is more evident in the external region. Moreover, the water influence on the mechanical property of shale was analyzed by comparing the failure characteristics of specimens with and without hydration; the CT results show that the hydration treatment affects the failure mode of shale. In addition, a new method based on the CT image was proposed to calculate the lateral strain; the evolution of the axial stress-strain curve reveals that the expansion phenomena exist before failure, and the evolution process includes four stages. At last, the differences in the deformation degree were discussed by analyzing the volumetric strain at different CT scanning layers; the results reflect the expansion or compression degree along with the specimen height. Those conclusions can help us further understand the water effect and the deformation characteristics of shale oil and gas reservoirs.

Pixel-wise radiometric calibration approach for infrared focal plane arrays using multivariate polynomial correction

Advances in infrared focal plane arrays allow thermal cameras to operate without active cooling equipment, however, the outputs of uncooled thermal cameras are closely related to their own temperatures, which degrades the temperature retrieval accuracy when ambient environment varies. In this paper, a novel radiometric calibration approach is proposed to decrease the errors derived from ambient temperature drift and internal heating. Firstly, a multivariate polynomial correction plus a second-order polynomial correction are implemented to stabilize the camera output derived from temporal non-uniformity. Afterwards, multi-point correction is applied to remove the fixed pattern noise caused by spatial non-uniformity. Finally, object temperature is calculated from thermal image gray values using the Planck-like approximation function. Experimental results demonstrated that the proposed method outperforms existing shutterless and shutter-based methods especially when uncooled thermal cameras are in unstable status.

Perspective Shape-from-Shading Problem: A Unified Convergence Result for Several Non-Lambertian Models.

Shape-from-Shading represents the problem of computing the three-dimensional shape of a surface given a single gray-value image of it as input. In a recent paper, we showed that the introduction of an attenuation factor in the brightness equations related to various perspective Shape-from-Shading models allows us to ensure the well-posedness of the corresponding differential problems. Here, we propose a unified convergence result of a numerical scheme for several non-Lambertian reflectance models. This result is interesting since it can be easily extended to other non-Lambertian models in a unified and, therefore, powerful framework.

Retraction Note: Agricultural super green image segmentation method based on Gaussian mixture model combined with Camshift

The Gaussian mixture model is used to determine the coordinate distribution curve of the image gray value. The distribution probability value and approximate spatial coordinates of the image gray value in different intervals are calculated by substituting the specified data into the model. When the approximate spatial coordinates are determined, establishing a second-order entropy function is used to calculate the coordinate distribution set of different levels of gray in a specific range, and the gray with the most considerable entropy function is selected as the distinction among all grays. The objectives are proved through a large number of simulation experiments that the method of establishing an entropy function to determine the spatial distribution of image gray levels can improve the accuracy of image segmentation and enhance image processing efficiency. The Camshift algorithm is frequently used when using the entropy function to determine the spatial distribution of image gray levels. At present, the algorithm is widely used in image processing and widely used by researchers in the research of computer data processing and robot target tracking. However, this algorithm has a fatal flaw. Suppose the color of the environment where the target is tracked is very similar to the color of the target itself or is prone to interference. In that case, it is straightforward to lose the target during target tracking. Researchers put forward a target tracking method based on ORB feature detection and Kalman filtering multiple algorithms based on this phenomenon. First, detect the target ORB feature points to initialize the search window, and then use the Kalman filter as the target motion state prediction mechanism. Suppose it is interfered with by the background. In that case, the ORB algorithm is used to match the Kalman prediction area’s feature points and the target model in the current frame, and the position of the target in the video frame is detected again. The predictor parameters are updated according to the Kalman filter to predict the target’s possible position after being occluded by the object.

Detection of Body Behavior Characteristics in Sports Training Based on Grey Relational Model

In order to deal with the problems of low detection accuracy and efficiency existing in traditional detection methods of body behavior characteristics in sports training, this paper proposes a new detection method of body behavior characteristics in sports training based on the grey correlation model. According to this method, the foreground binary image is obtained by the interframe difference method, and the action information and contour information are extracted, firstly. And then, the behavior feature descriptor is obtained. After the description of body behavior characteristics in sports training is completed, the grey correlation coefficient is calculated, and the complete observation equation of time delay estimation of image grey value under complex sports training background is established to complete the detection of body behavior characteristics. The experimental results show that, compared with the traditional methods, the detection accuracy and efficiency of the text method can always maintain a high level, indicating that the practical application performance of this method is strong. In fact, the grey analysis does not attempt to find the best solution but does provide techniques for determining a good solution.

Fast image inpainting strategy based on the space-fractional modified Cahn-Hilliard equations

The solution strategy of the space-fractional modified Cahn-Hilliard equation as a tool for the gray value image inpainting model is studied. The existing strategies solve the convexity splitting scheme of the vector-valued Cahn-Hilliard model by Fourier spectral method. In this paper, we constructed a fast solver for the discretized linear systems possessing the saddle-point structure within block-Toeplitz-Toeplitz-block (BTTB) structure arising from the 2D space-fractional modified Cahn-Hilliard equation. The new solver enjoys computational advantage since circulant approximation and fast Fourier transforms (FFTs) can be used for solving the involved linear subsystems. Theoretical analysis shows the spectrum of the preconditioned matrix clusters around 1, which implies the fast convergence rate of the proposed preconditioner. Numerical examples are given to confirm the effectiveness of our method.

Visible and infrared missile-borne image registration based on improved SIFT and joint features

For the acquired visible and infrared missile-borne images, in order to provide accurate and reliable image data for the subsequent processing of images to achieve complementary information, we carried out visible and infrared missile-borne image registration research. Firstly, the shape context is used to extract the rough edge features of the image. Then we improve the SIFT gradient definition to overcome the difference in image gray value, and combine with the coherent point drift algorithm to increase the number of correct matching points to achieve global image registration. Experimental results show that, compared with the existing registration methods, the proposed method has a good visual effect of registration, greatly improves the correct registration points and registration accuracy, and can better solve the problem of visible infrared missile-borne image registration.