Discrete Image Research Articles

Developing surface plasmon resonance imaging for discrete particle detection based on a silver layer coated with polyacrylic acid/iodine polyelectrolyte brushes

The detection and analysis of low concentrations of chemical and biological particles presents an enduring challenge in scientific exploration. Among the various techniques employed for this purpose, surface plasmon resonance (SPR) stands as a powerful label-free method with a wide-range applications in advanced detection and sensing. Traditional SPR, while highly effective, encounters inherent limitations when it comes to scrutinizing individual particles. To overcome this limitation, wide-field surface plasmon resonance microscopy emerges as a promising approach, offering real-time detection capabilities for suspended particles in solution. In this study, an innovative wide-field surface plasmon resonance microscope is presented, strategically combining a silver layer coated with polyelectrolyte brushes—polyacrylic acid/iodine, to enhance the detection sensitivity and mitigate silver’s susceptibility to oxidation. It is demonstrated that coating the silver layer with polyacrylic acid/iodine enhances the sensitivity of discrete particle imaging with a high spatial resolution of the recorded image. Since wide-field surface plasmon resonance microscopy can detect discrete particles, a mathematical model is proposed to describe the SPR sensing mechanism based on discrete particles for precisely characterizing and interpreting the experimental observations. This work demonstrates a capability for comprehensive analysis of low concentrations of chemical and biological particles at the single particle level.

Lensing Point-spread Function of Coherent Astrophysical Sources and Nontrivial Wave Effects

Most research on astrophysical lensing has been conducted using the geometric optics framework, where there exists a clear concept of lensing images. However, wave optics effects can be important for coherent sources, e.g., pulsars, fast radio bursts, and gravitational waves observed at long wavelengths. There, the concept of lensing images needs an extension. We introduce the concept of the “lensing point-spread function” (LPSF), the smoothed flux density distribution of a coherent point source after being lensed, as a generalization of the lensing image concept at finite frequencies. The frequency-dependent LPSF captures the gradual change of the flux density distribution of the source from discrete geometric images at high frequencies to a smooth distribution at low frequencies. It complements other generalizations of lensing images, notably the imaginary images and the Lefschetz thimbles. Being a footprint of a lensing system, the LPSF is useful for theoretical studies of lensing. Using the LPSF, we identify a frequency range with nontrivial wave effects, where both geometric optics and perturbative wave optics fail, and determine this range to be ∣κ∣−1 ≲ ν ≲ 10, with κ and ν being the dimensionless lens amplitude and the reduced observing frequency, respectively. Observation of LPSFs with nontrivial wave effects requires either very close-by lenses or very large observing wavelengths. The potential possibilities are the lensing of gravitational waves, the plasma lensing of Milky Way pulsars, and lensing by the solar gravitational lens.

Study protocol for the iMarkHD study in individuals with Huntington's disease

Background: Huntington's disease (HD) is still often defined by the onset of motor symptoms, inversely associated with the size of the CAG repeat expansion in the huntingtin gene. Although the cause of HD is known, much remains unknown about mechanisms underlying clinical symptom development, disease progression, and specific clinical subtypes/endophenotypes. Objective: In the iMarkHD study, we aim to investigate four discrete molecular positron emission tomography (PET) tracers and magnetic resonance imaging (MRI) markers as biomarkers for disease and symptom progression. Methods: Following MRI optimization in five healthy volunteers (cohort 1), we aim to recruit 108 participants of whom 72 are people with HD (PwHD) and 36 healthy volunteers (cohort 2). Pending interim analysis, these numbers could increase to 96 PwHD and 48 healthy controls. Participants will complete a total of 10 study visits, consisting of a screening visit followed by a clinical and MRI visit and PET visits at baseline, year 1, and year 2. PET targets include the cannabinoid 1, histamine 3, and serotonin 2A receptors, and phosphodiesterase 10A, whereas MRI will be multimodal, including, but not limited to, the assessment of cerebral blood flow, functional connectivity, and brain iron. Results: Recruitment is currently active and started in September 2022. Conclusions: By combining PET and multi-modal MRI assessments we expect to provide a comprehensive examination of the molecular, functional, and structural framework of HD progression. As such, the iMarkHD study will provide a solid base for the identification of treatment targets and novel outcome measures for future clinical trials.

Estimation of the surface impedance based on the discrete complex image source method and array processing

This paper introduces a method for estimating the sound absorption coefficient of a large panel of absorbing material from measurements using a microphone array. The method relies upon describing the pressure field as the set of monopoles obtained when the reflection coefficient is represented as the Laplace Transform of an image source distribution. This formulation results in a well-behaved integral equation, which is discretized using Gaussian quadrature to yield an inverse problem. It is possible to determine the regularized solution to the inverse problem and reconstruct the surface impedance of the sample by conducting measurements of the sound pressure at multiple points of observation. This sound field representation is adequate for sound sources in the close range of the material and the array. The paper presents simulated and experimental investigations to delineate the efficacy of the proposed measurement technique against the well-known image source method, thereby highlighting its significant potential to advance acoustic characterization methodologies.

Approximation theory of wavelet frame based image restoration

In this paper, we analyze the error estimate of a wavelet frame based image restoration method from degraded and incomplete measurements. We present the error between the underlying original discrete image and the approximate solution which has the minimal ℓ1-norm of the canonical wavelet frame coefficients among all possible solutions. Then we further connect the error estimate for the discrete model to the approximation to the underlying function from which the underlying image comes.

A Vision-Language Model-Based Traffic Sign Detection Method for High-Resolution Drone Images: A Case Study in Guyuan, China.

As a fundamental element of the transportation system, traffic signs are widely used to guide traffic behaviors. In recent years, drones have emerged as an important tool for monitoring the conditions of traffic signs. However, the existing image processing technique is heavily reliant on image annotations. It is time consuming to build a high-quality dataset with diverse training images and human annotations. In this paper, we introduce the utilization of Vision-language Models (VLMs) in the traffic sign detection task. Without the need for discrete image labels, the rapid deployment is fulfilled by the multi-modal learning and large-scale pretrained networks. First, we compile a keyword dictionary to explain traffic signs. The Chinese national standard is used to suggest the shape and color information. Our program conducts Bootstrapping Language-image Pretraining v2 (BLIPv2) to translate representative images into text descriptions. Second, a Contrastive Language-image Pretraining (CLIP) framework is applied to characterize not only drone images but also text descriptions. Our method utilizes the pretrained encoder network to create visual features and word embeddings. Third, the category of each traffic sign is predicted according to the similarity between drone images and keywords. Cosine distance and softmax function are performed to calculate the class probability distribution. To evaluate the performance, we apply the proposed method in a practical application. The drone images captured from Guyuan, China, are employed to record the conditions of traffic signs. Further experiments include two widely used public datasets. The calculation results indicate that our vision-language model-based method has an acceptable prediction accuracy and low training cost.

Discrete Wavelet Transform-Based Image Processing: A Review

The field of image processing has seen remarkable advancements over the past few decades, and Discrete Wavelet Transform (DWT) has emerged as a powerful tool with in this domain. This review article provides a comprehensive overview of previously published works that focus on DWT’s application in image processing. DWT offers multi-resolution analysis capabilities, making it particularly useful for various image processing tasks such as de-noising, compression, enhancement, and feature extraction. This review explores the fundamental principles of DWT and its mathematical foundations. We delve into its advantages over traditional Fourier Transform methods, particularly its ability to handle non-stationary signals and provide both frequency and spatial information simultaneously. The article surveys the application of DWT in different image processing techniques. It discusses how DWT contributes to the efficiency and effectiveness of image compression algorithms, such as the JPEG 2000 standard, and its role in reducing noise while preserving important image features.

High‐resolution incoherent interference imaging without phase measurement

AbstractAiming for long‐distance, high‐resolution, passive imaging, we use fiber coupling to replace the spatial coupling of the segmented planar imaging detector for electro‐optical reconnaissance imaging system and propose a novel incoherent interference imaging method, which avoids the problem that the resolution of the imaging system is limited by the size of the silicon wafer. The method uses a phase retrieval algorithm and does not need to measure the phase in the system, effectively avoiding the intrinsic jitter problem of fiber optics. The feasibility of the method in achieving target image reconstruction was verified through simulation; furthermore, an indoor two‐dimensional discrete sampling imaging experiment for a simple four‐rod target was conducted, and an outdoor one‐dimensional feature identification experiment for a two‐rod target was also performed. The results showed that the resolution exceeds the diffraction limit and the reconstruction error of target size is less than 3.5%.

Deep learning MR reconstruction in knees and ankles in children and young adults. Is it ready for clinical use?

To evaluate the diagnostic performance and image quality of accelerated Turbo Spin Echo sequences using deep-learning (DL) reconstructions compared to conventional sequences in knee and ankle MRIs of children and young adults. IRB-approved prospective study consisting of 49 MRIs from 48 subjects (10 males, mean age 16.4years, range 7-29years), with each MRI consisting of both conventional and DL sequences. Sequences were evaluated blindly to determine predictive values, sensitivity, and specificity of DL sequences using conventional sequences and knee arthroscopy (if available) as references.Physeal patency and appearance were evaluated. Qualitative parameters were compared. Presence of undesired image alterations was assessed. The prevalence of abnormal findings in the knees and ankles were 11.7% (75/640), and 11.5% (19/165), respectively. Using conventional sequences as reference, sensitivity and specificity of DL sequences in knees were 90.7% and 99.3%, and in ankles were 100.0% and 100.0%. Using arthroscopy as reference, sensitivity and specificity of DL sequences were 80.0% and 95.8%, and of conventional sequences were 80.0% and 97.9%. Agreement of physeal status was 100.0%. DL sequences were qualitatively "same-or-better" compared to conventional (p < 0.032), except for pixelation artifact for the PDFS sequence (p = 0.233). No discrete image alteration was identified in the knee DL sequences. In the ankle, we identified one DL artifact involving a tendon (0.8%, 1/125). DL sequences were faster than conventional sequences by a factor of 2 (p < 0.001). In knee and ankle MRIs, DL sequences provided similar diagnostic performance and "same-or-better" image quality than conventional sequences at half the acquisition time.

Discrete frequency infrared-guided image for microplastic analysis: Performance and limitations

Applying a quantum cascade laser as a light source in a benchtop infrared spectrometer has enabled a new strategy for microplastic analysis. This strategy uses discrete frequency infrared images to recognize particles from a reflecting substrate prior to chemical identification, thereby improving analysis throughput.This research work established the performance of the approach using an Agilent Laser Direct Infrared chemical imaging system (LDIR). Over 90 % of fluorescently labelled polyethylene particles (> 20 µm) spiked in the environmental matrix were correctly identified at scanning wavenumbers and sensitivity settings. When the high-throughput setting was used, the discrete frequency infrared image-guided microplastic analysis demonstrated good selectivity. With around 30 % of particles in the sample scanned, the technique could recognize around 86 % percent of fluorescently labelled polyethylene particles introduced.The MirrIR low-e slide and gold-coated filter are suitable reflecting substrates for this particle analysis strategy. When detecting small microplastic particles with sizes between 5 and 20 µm, around 94 % spiked polymethyl methacrylate particles could be found on the MirrIR low-e slide. The analysis also showed good accuracy and precision in a three-day test. The texture on the gold-coated filter surface could interfere with the small particle analysis.Overall, this novel discrete frequency infrared image-guided particle analysis shows good accuracy, high precision, and respectable selectivity, which are all critical characteristics for microplastic analysis.

Particle Tracking of Orientation and Velocity Of Arbitrary Shape

Dispersions or particulate systems are prevalent in numerous natural and practical applications. Characterisation of such multiphase systems usually involves measuring size, spatial location, velocity and number density, which is usually non-trivial. The 'Depth from Defocus' (DFD) technique provides an imaging-based volumetric approach for estimating the size and position of dispersed spherical particles using the blurring information from a shadowgraph image. The two-image DFD approach involves two cameras to capture simultaneous images at different degrees of blur, which, although simpler, requires a mandatory calibration procedure. This study proposes a single-image DFD technique that provides a calibration-free size estimation using theoretical functions while evaluating position requires a simple calibration. The efficacy of this technique is demonstrated for various sparse spherical dispersions, including target dots, dispersed glass beads, liquid droplets in sprays and surface bubble rupture, and pollen grains. The method further enables precise determination of the measurement volume, which is crucial, allowing for bias-free estimates of the size distribution and volume concentration. The simple optical configuration and semi-automatic calibration procedure make this method easily deployable and accessible for diverse applications. The pixelation effect on the limiting value of the degree of blur is also estimated theoretically and compared with experiments. This effect is of interest concerning any typical discrete sensor imaging system in general.

Evaluation of Fatigue Behavior of Asphalt Field Cores Using Discrete Element Modeling.

Fatigue cracking is one of the primary distresses of asphalt pavements, which significantly affects the asphalt pavement performance. The fatigue behavior of the asphalt mixture observed in the laboratory test can vary depending on the type of fatigue test and the dimension and shape of the test specimen. The variations can make it difficult to accurately evaluate the fatigue properties of the field asphalt concrete. Accordingly, this study proposed a reliable method to evaluate the fatigue behavior of the asphalt field cores based on discrete element modeling (DEM). The mesoscopic geometric model was built using discrete element software PFC (Particle Flow Code) and CT scan images of the asphalt field cores. The virtual fatigue test was simulated in accordance with the semi-circular bending (SCB) test. The mesoscopic parameters of the contacting model in the virtual test were determined through the uniaxial compression dynamic modulus test and SCB test. Based on the virtual SCB test, the displacement, contact forces, and crack growth were analyzed. The test results show that the fatigue life simulated in the virtual test was consistent with that of the SCB fatigue test. The fatigue cracks in the asphalt mixture were observed in three stages, i.e., crack initiation, crack propagation, and failure. It was found that the crack propagation stage consumes a significant portion of the fatigue life since the tensile contact forces mainly increase in this stage.

Flow control method in concrete particles pneumatic sweeping process based on image recognition and OPC protocol

Based on image recognition and OPC(Object linking and embedding for Process Control) protocol, an intelligent pneumatic sweeping scheme is proposed to solve the problem of pavement aggregates cleaning. Firstly, the effects of four different auxiliary light sources and six different image segmentation methods on the recognition of single particle images were analyzed, and the maximum entropy segmentation method was the best under white light. Secondly, the effect of four different filtering methods on discrete multi-particle image recognition was studied. Among them, the adaptive median filter can better solve the problem of interference points misidentified as particles. The recognition effect of four different edge detection methods on aggregate particle pile was analyzed. And Canny operator with better effect was finally used to recognize particle pile from the overhead and side view. Finally, the communication mode between the host computer and the controller established based on OPC protocol was verified by experiments.

A Long-time-series Spatio-Temporal-Spectral Fusion Method via Multi-task Learning

Abstract. Due to the limitations of sensor hardware, clouds and fog, and data transmission limitations, it is difficult for the data obtained by spaceborne remote sensing imager to achieve high temporal, spatial and spectral resolution at the same time, which limits its application in long-time-series high-frequency monitoring. At present, there are several spatio-temporal-spectral algorithms that can realize the fusion of temporal, spatial and spectral resolution, but most of them are based on one to two discrete images, and the integrated fusion at the multi-dimensional level has not yet been realized. There is currently no research on the spatio-temporal-spectral fusion method based on LONG-TIME-SERIES multi-scene remote sensing data. Aiming at solving the bottleneck of spatio-tempora-spectral resolution of remote sensing data, this study proposes a new long-time-series spatio-temporal-spectral fusion method based on multi-task learning to realize the multi-dimensional optimization of multi-source remote sensing data resolutions. Experiments used simulated and real datasets, both of which contain 4 images of 10m ZY1-02D multispectral data, 7 images of 16m GF-6 multispectral data and 4 images of 30m ZY1-02D hyperspectral data, and obtained 7 images of 10m hyperspectral data. The experiments The results show that our method performs the best compared to other methods. This method can provide effective data support for applications based on long-time series remote sensing data.

Determining a subset of discrete emotion-evoking images from the international affective picture system in a Turkish sample

The International Affective Picture System (IAPS), widely utilized in emotion research, is based on a dimensional approach. It includes 1196 colored static images depicting different semantic categories, with valence, arousal, and dominance ratings determined for each. However, the specific discrete emotions evoked by these images within the IAPS has remained unclear. Therefore, numerous investigations have been conducted in different cultural contexts to address this matter. This study aimed to determine a subset of images from the IAPS that elicit discrete emotions. To achieve this goal, an image was selected for each semantic category within the IAPS, employing specific criteria, and the elicited discrete emotions were subsequently examined in a Turkish sample. Additionally, valence ratings for these images were obtained within the Turkish culture, facilitating cross-cultural comparisons. Sixty Turkish students (43 female) aged between 19-25 participated in this study. The participants rated the valence, discrete emotion category, and the intensity of the determined discrete emotion (1 = none; 9 = very strongly) for 231 selected images from the IAPS, respectively. Valence ratings were obtained using the paper-pencil version of the Self-Assessment Manikin. Accordingly, 76 images, of which intensity ratings above six were classified into a single discrete emotion with the agreement of 70% and above among the participants. Furthermore, the obtained data were compared with the results of studies conducted in different cultural settings to explore potential cultural differences. Overall, the results highlighted the importance of selecting culture-specific stimuli in emotion studies.

Application of generalised atomic wavelets in image processing problems

The problem of processing large data arrays is considered. Development of effective algorithms is the basis of successful application of new technologies. Complexity of the algorithm and accuracy of the results are the key quality indicators. The use of compact carrier functions leads to a decrease in processing time and complexity of the numerical algorithm. The accuracy of the data representation and the correctness of the results mainly depend on the approximation properties. It follows that a combination of the above characteristics is necessary to create efficient algorithms for processing large data sets. Aim of article – to develop and evaluate the possibilities of application of generalised wavelets based on Kravchenko-Rvachev functions for image processing with a given quality on the basis of the analysis of the quality loss control mechanism. Discrete image compression by Kravchenko-Rvachev functions possessing such fundamental qualities as good approximation properties, high order of smoothness and existence of a basis with a local carrier in spaces of atomic functions is considered. Since the generalised up-functions have the same properties, similar compression results can be obtained using the generalised discrete atomic transform, which is based on their application, which is a promising direction in digital image processing. The presented method of image processing can be used for various purposes, including reducing the size of data before their transmission through the communication channel from the sensor to the point of reception via a communication line with limited bandwidth, for storing the received images for their further use in the centres of information processing of remote sensing of the earth, as well as for data transmission to potential users.

Process-Structure-Property Relationship Development in Large-Format Additive Manufacturing: Fiber Alignment and Ultimate Tensile Strength.

Parts made through additive manufacturing (AM) often exhibit mechanical anisotropy due to the time-based deposition of material and processing parameters. In polymer material extrusion (MEX), printed parts have weak points at layer interfaces, perpendicular to the direction of deposition. Poly(lactic acid) with chopped carbon fiber was printed on a large-format pellet printer at various extrusion rates with the same tool pathing to measure the fiber alignment with deposition via two methods and relate it to the ultimate tensile strength (UTS). Within a singular printed bead, an X-ray microscopy (XRM) scan was conducted to produce a reconstruction of the internal microstructure and 3D object data on the length and orientation of fibers. From the scan, discrete images were used in an image analysis technique to determine the fiber alignment to deposition without 3D object data on each fiber's size. Both the object method and the discrete image method showed a negative relationship between the extrusion rate and fiber alignment, with -34.64% and -53.43% alignment per extrusion multiplier, respectively, as the slopes of the linear regression. Tensile testing was conducted to determine the correlation between the fiber alignment and UTS. For all extrusion rates tested, as the extrusion multiplier increased, the percent difference in the UTS decreased, to a minimum of 8.12 ± 14.40%. The use of image analysis for the determination of the fiber alignment provides a possible method for relating the microstructure to the meso-property of AM parts, and the relationship between the microstructure and the properties establishes process-structure-property relationships for large-format AM.

An online color and shape integrated detection method for flexible packaging surface defects

It is difficult for the spectrophotometer to meet the requirement of real-time color defect detection for flexible packaging prints. The false of shape defect detection is caused by artifact interference and insufficient classification accuracy of defect classification network. A color defect detection method for flexible packaging is proposed, which realizes the adaptive adjustment of the correction parameters of the Commission Internationale de l´Eclairage Delta E 2000 (CIEDE2000) equations with the detection object. It improves the speed and accuracy of the color defect detection for flexible packaging. An quadratic difference strategy is designed for template matching subtraction method to remove artifact interference. A method for enhancing shape defect data set of flexible packaging is proposed. Using discrete images of defects as network input, self-attention mechanism and spectral normalization methods are added to the deep convolutional generative adversarial networks (DCGAN) to enhance the effective dataset for the training of defect classification network. The accuracy of color detection for flexible packaging prints is improved by 38.7% based on optimized CIEDE2000. The average structure similarity index measure (SSIM) value of the improved DCGAN for defect detection is 0.845, and the Fréchet inception distance (FID) is 121.463. It takes 83.63 ms for the color and shape integrated detection method to detect shape defects on flexible packaging surfaces with an accuracy of 98.3%. The online color and shape integrated detection method can be applied to automated flexible packaging workshops to achieve real-time defect detection.

SparseGNV: Generating Novel Views of Indoor Scenes with Sparse RGB-D Images

We study to generate novel views of indoor scenes given sparse input views. The challenge is to achieve both photorealism and view consistency. We present SparseGNV: a learning framework that incorporates 3D structures and image generative models to generate novel views with three modules. The first module builds a neural point cloud as underlying geometry, providing scene context and guidance for the target novel view. The second module utilizes a transformer-based network to map the scene context and the guidance into a shared latent space and autoregressively decodes the target view in the form of discrete image tokens. The third module reconstructs the tokens back to the image of the target view. SparseGNV is trained across a large-scale indoor scene dataset to learn generalizable priors. Once trained, it can efficiently generate novel views of an unseen indoor scene in a feed-forward manner. We evaluate SparseGNV on real-world indoor scenes and demonstrate that it outperforms state-of-the-art methods based on either neural radiance fields or conditional image generation.

Transient Glimpses: Unveiling Occluded Backgrounds through the Spike Camera

The de-occlusion problem, involving extracting clear background images by removing foreground occlusions, holds significant practical importance but poses considerable challenges. Most current research predominantly focuses on generating discrete images from calibrated camera arrays, but this approach often struggles with dense occlusions and fast motions due to limited perspectives and motion blur. To overcome these limitations, an effective solution requires the integration of multi-view visual information. The spike camera, as an innovative neuromorphic sensor, shows promise with its ultra-high temporal resolution and dynamic range. In this study, we propose a novel approach that utilizes a single spike camera for continuous multi-view imaging to address occlusion removal. By rapidly moving the spike camera, we capture a dense stream of spikes from occluded scenes. Our model, SpkOccNet, processes these spikes by integrating multi-view spatial-temporal information via long-short-window feature extractor (LSW) and employs a novel cross-view mutual attention-based module (CVA) for effective fusion and refinement. Additionally, to facilitate research in occlusion removal, we introduce the S-OCC dataset, which consists of real-world spike-based data. Experimental results demonstrate the efficiency and generalization capabilities of our model in effectively removing dense occlusions across diverse scenes. Public project page: https://github.com/Leozhangjiyuan/SpikeDeOcclusion.