Image Pixel Intensity Research Articles

Moving target segmentation method based on tensor decomposition and graph Laplacian regularization

A new method for segmenting the target foreground from image frames is proposed by utilizing the theory of graph signal processing and the tensor decomposition model aiming at the problem that the segmentation results of the existing foreground segmentation methods in image frames under dynamic scenes are not high in accuracy. The intrinsic connection between image pixels in each frame of an image sequence is modeled as a graph, the image pixel intensities are modeled as graph signals, and the correlation between pixels is characterized by the graph model. According to the significant difference between the dynamic background and the target change in the moving foreground in the image sequence, the dynamic background region in each image frame is smoothed and suppressed, and the disturbing information of the dynamic background is transformed into the useful component information in the low-rank subspace. The connectivity between image pixels can be characterized by the graph Laplacian regularization term, and then the target foreground segmentation problem in the image sequence is equivalent to a constrained optimization problem with tensor decomposition and graph Laplacian regularization term. The alternating direction multiplier method is used to solve the optimization problem, and the simulation results on real scene data set verify the effectiveness of the algorithm.

Finger Vein Recognition Based on Unsupervised Spiking Convolutional Neural Network with Adaptive Firing Threshold.

Currently, finger vein recognition (FVR) stands as a pioneering biometric technology, with convolutional neural networks (CNNs) and Transformers, among other advanced deep neural networks (DNNs), consistently pushing the boundaries of recognition accuracy. Nevertheless, these DNNs are inherently characterized by static, continuous-valued neuron activations, necessitating intricate network architectures and extensive parameter training to enhance performance. To address these challenges, we introduce an adaptive firing threshold-based spiking neural network (ATSNN) for FVR. ATSNN leverages discrete spike encodings to transforms static finger vein images into spike trains with spatio-temporal dynamic features. Initially, Gabor and difference of Gaussian (DoG) filters are employed to convert image pixel intensities into spike latency encodings. Subsequently, these spike encodings are fed into the ATSNN, where spiking features are extracted using biologically plausible local learning rules. Our proposed ATSNN dynamically adjusts the firing thresholds of neurons based on average potential tensors, thereby enabling adaptive modulation of the neuronal input-output response and enhancing network robustness. Ultimately, the spiking features with the earliest emission times are retained and utilized for classifier training via a support vector machine (SVM). Extensive experiments conducted across three benchmark finger vein datasets reveal that our ATSNN model not only achieves remarkable recognition accuracy but also excels in terms of reduced parameter count and model complexity, surpassing several existing FVR methods. Furthermore, the sparse and event-driven nature of our ATSNN renders it more biologically plausible compared to traditional DNNs.

High-fidelity metasurface holography illuminated by an ultra-uniform flat-top beam generated through active wavefront shaping.

We present the first, to our knowledge, metasurface holographic display method with exceptional fidelity and minimal edge noise, based on highly uniform flat-top light generated by a digital micromirror device (DMD). Based on the error-diffusion algorithm and iterative refinement process, the amplitude distribution of the initial Gaussian light was dynamically closed-loop modulated, and the standard difference of the intensity of the 3 mm diameter center flat-top beam was reduced to less than 3.4%. The holographic image generated by a flat-top beam illuminated metasurface using this method exhibited a reduction in the brightness coefficient of variation (CV) from 0.82 to 0.58 and a reduction in the contrast difference (CD) of an image pixel intensity from 240 to 160. This method can efficiently improve the quality and uniformity of flat-top light, developing a new perspective for high resolution and fidelity metasurface holographic display by modulating a light source.

Calibration of a gamma ray Compton camera for radioactivity measurements

A dual-plane Compton imaging detector previously developed for prompt gamma imaging has been further tested and calibrated for quantitative radioactivity determination, specifically to assess radioactive debris from a reactor fuel element. The debris piece contains uranium isotopes and several long-lived fission products, including cesium-137 (Cs-137). The detector consists of two pixelated planes of cadmium-zinc-telluride (CZT) crystals which record gamma ray events interacting within these volumes. In addition to the energy spectrum obtained by pulse height analysis, the time-stamped events from each detector pixel are sorted by location into 3 categories for post-processing: intra-plane, inter-plane, and coincidence. An image reconstruction software enables spatial localization of the select gamma ray peak(s) from the spectrum, thereby separating the gamma ray by specific isotopes. We used the camera to image a small piece of debris for demonstration purposes. A measurement was performed alongside a high purity germanium (HPGe) detector, which could determine the isotopes' absolute activities and be used as a reference for the camera. In addition, a known isotopic point source was used to calibrate the camera’s energy detection efficiency in the same measurement geometry, correlating the image pixel intensity to the isotopic activity.

Quantitative assessment of the visual quality of digital images based on the laws of human visual perception

The existing methods of quantitative assessment of the visual quality of digital images are studied. Among the main shortcomings of the studied methods, the following can be singled out. Most of them require a reference image, do not include all the components that affect visual quality and do not take into consideration the laws of human visual perception. It was decided to develop a method for quantitative assessment of the visual quality of images, which will work without a reference image and will take into account the regularities of human visual perception. There are characterized the main regularities of human visual perception, which are used in the development of the technique. A classification of the researched methods of quantitative assessment of image quality is proposed for structuring their analysis. It was decided to investigate methods of quantitative assessment of quality based on statistical analysis of image pixel intensities. There are described factors affecting the quality of images and methods of their control based on changes in the pixel intensity distribution histogram. A generalized expression of quantitative quality assessment based on moments is proposed. A methodology for quantitative assessment of the visual quality of images has been developed, which does not require a reference image and is based on the laws of human visual perception. This method was tested on an image processed by local contrast enhancement and low-pass filtering. The test results showed that the visual perception of image quality coincides with the quantitative assessment of its quality. It is possible to use the proposed method with some modifications to determine the quality of color images. Moreover, a potential avenue for advancing the proposed method involves adapting it for evaluating images afflicted by distortions induced by noise presence.

Method for solving atmospheric dispersion parameters of radioactive aerosols based on wind-tunnel laser measurement experiments

Method for solving atmospheric dispersion parameters of radioactive aerosols based on wind-tunnel laser measurement experiments

VLSI implementation of image compressor using probabilistic run length coding

AbstractMultimedia applications, such as image processing including image and video transfer, heavily rely on reduction. The traditional device methods to picture reduction use more space, energy, and processing time. The majority of current efforts use the Golomb‐Rice encoding, due to its larger memory requirement and higher computing difficulty. So, this research concentrated on hardware design‐oriented probability run length (PRL) coding technique based on lossless colour image compression. The block truncation coding (BTC) features of the compression process are used by the suggested PRL method. The proposed image compression hardware consists of various modules such as a Parameter calculator, fuzzy table, bitmap generator, BTC parameters training, prediction, and error control, and PRL‐based finite state machine (PRL‐FSM). The proposed image compressor utilizes the parameter calculator block, which estimates the block type based on the image pixel intensities for each sub‐block. Thus, each block of the image is compressed by using a new block type and generates a variable block size. The proposed method utilizes the PRL‐BTC encoding method, which also calculates the probability of error between the compressed image to the test image. The process is iterated until the performance trade‐off between hardware cost and compression ratio (CR) is achieved. Hence, both smooth regions and non‐smooth regions of images are perfectly compressed by the probability‐based block selection. The simulation results show that the proposed method resulted in a better area, power, delay metrics, peak signal‐to‐noise ratio (PSNR), and CR compared to the state of art approaches.

DAGM-fusion: A dual-path CT-MRI image fusion model based multi-axial gated MLP

DAGM-fusion: A dual-path CT-MRI image fusion model based multi-axial gated MLP

Two-dimensional image zooming by adaptive Sinc-based technique

This article reports a novel adaptive Sinc-based zooming technique. The theoretical basis of the zooming technique is the Whittaker–Shannon interpolation formula and the Nyquist–Shannon sampling theorem. The technique can be used to zoom out, zoom in, and reconstruct two-dimensional images with no zoom. The technique introduces a novel concept called pixel-local-scaled k-space, which is calculated as follows. The k-space magnitude and the bandwidth of the image to zoom are calculated. The bandwidth of the frequency components of the image to zoom is estimated by the difference between the maximum and the minimum numerical value of the k-space magnitude. The ratio between the standardised k-space magnitude of the image and the bandwidth is calculated pixel-by-pixel, and is called pixel-local-scaled k-space. The signal is reconstructed using a finite convolution between image pixel intensities and Sinc functions. The zooming factor is consequential to the numerical difference between the pixel-local-scaled k-space and the sampling rate. The novelty of this zooming technique is to be adaptive because the pixel-local-scaled k-space is a pixel-by-pixel map, that is, an image with the same number of pixels as the departing image. Adaptive Sinc-based zooming is validated through the comparison with non-adaptive Sinc-based zooming, and its smoothing effect is compared to the smoothing of a variety of image space and z-space filters. The adaptive Sinc-based zooming technique achieves image zooming reconstruction using sampling rates within an extended range versus the range allowed by non-adaptive Sinc-based zooming.

Pixel’s Quantum Image Enhancement Using Quantum Calculus

The current study provides a quantum calculus-based medical image enhancement technique that dynamically chooses the spatial distribution of image pixel intensity values. The technique focuses on boosting the edges and texture of an image while leaving the smooth areas alone. The brain Magnetic Resonance Imaging (MRI) scans are used to visualize the tumors that have spread throughout the brain in order to gain a better understanding of the stage of brain cancer. Accurately detecting brain cancer is a complex challenge that the medical system faces when diagnosing the disease. To solve this issue, this research offers a quantum calculus-based MRI image enhancement as a pre-processing step for brain cancer diagnosis. The proposed image enhancement approach improves images with low gray level changes by estimating the pixel’s quantum probability. The suggested image enhancement technique is demonstrated to be robust and resistant to major quality changes on a variety of MRI scan datasets of variable quality. For MRI scans, the BRISQUE “blind/referenceless image spatial quality evaluator” and the NIQE “natural image quality evaluator” measures were 39.38 and 3.58, respectively. The proposed image enhancement model, according to the data, produces the best image quality ratings, and it may be able to aid medical experts in the diagnosis process. The experimental results were achieved using a publicly available collection of MRI scans.

An investigation of the influence of microstructure surface topography on the imaging mechanism to explore super-resolution microstructure

Vision-based precision measurement is limited by the optical resolution. Although various super-resolution algorithms have been developed, measurement precision and accuracy are difficult to guarantee. To achieve nanoscale resolution measurement, a super-resolution microstructure concept is proposed which is based on the idea of a strong mathematical mapping relationship that may exist between microstructure surface topography features and the corresponding image pixel intensities. In this work, a series of microgrooves are ultra-precision machined and their surface topographies and images are measured. A mapping relationship model is established to analyze the effect of the microgroove surface topography on the imaging mechanism. The results show that the surface roughness and surface defects of the microgroove have significant effects on predicting the imaging mechanism. The optimized machining parameters are determined afterward. This paper demonstrates a feasible and valuable work to support the design and manufacture super-resolution microstructure which has essential applications in precision positioning measurement.

Fluorescent Tracer Particles For PIV Measurement In Gas Flows

This paper presents the development and employment of fluorescent tracer particles which allow removal of undesired strong light reflections in gas-phase Particle Image Velocimetry measurements. A spectroscopic investigation on fluorescent dye-doped solutions at different concentrations provided fluorescent emission spectra ranging their peak between 540 and 585 nm when excited by 527 nm wavelength Nd:YLF laser. The fluorescence intensity of the solutions in a form of sub-micron droplet was examined through comparison of statistical pixel intensities of raw particle images. Within the investigated concentration range, the intensity was found to be almost proportional to the dye concentration. The developed particle was tested in a wind tunnel at 5 m/s mean flow speed with a metal turbine blade placed in the channel and laser sheet oriented to impinge the blade surface. With the employment of an adequate optical band-stop filter, the undesired light reflections were successfully removed and reasonable vector calculations were enabled in proximity to the reflective surfaces. Lastly, the fluorescent PIV was performed in a high-speed short-duration rotating turbine test rig in order to demonstrate the effectiveness of the technique in industrial or extreme applications comprise severe conditions.

Dynamic Multi-projection Mapping Based on Parallel Intensity Control.

Projection mapping using multiple projectors is promising for spatial augmented reality; however, it is difficult to apply it to dynamic scenes. This is because the conventional method decides all pixel intensities of multiple images simultaneously based on the global optimization method, and it is hard to reduce the latency from motion to projection. To mitigate this, we propose a novel method of controlling the intensity based on a pixel-parallel calculation for each projector in real-time with low latency. This parallel calculation leverages the insight that the projected pixels from different projectors in overlapping areas can be approximated independently if the pixel is sufficiently small relative to the surface structure. Additionally, our pixel-parallel calculation method allows a distributed system configuration, such that the number of projectors can be increased to form a network for high scalability. We demonstrate a seamless mapping into dynamic scenes at 360 fps with a 9.5-ms latency using ten cameras and four projectors.

Heterogeneous cryo-EM projection image classification using a two-stage spectral clustering based on novel distance measures.

Single-particle cryo-electron microscopy (cryo-EM) has become one of the mainstream technologies in the field of structural biology to determine the three-dimensional (3D) structures of biological macromolecules. Heterogeneous cryo-EM projection image classification is an effective way to discover conformational heterogeneity of biological macromolecules in different functional states. However, due to the low signal-to-noise ratio of the projection images, the classification of heterogeneous cryo-EM projection images is a very challenging task. In this paper, two novel distance measures between projection images integrating the reliability of common lines, pixel intensity and class averages are designed, and then a two-stage spectral clustering algorithm based on the two distance measures is proposed for heterogeneous cryo-EM projection image classification. In the first stage, the novel distance measure integrating common lines and pixel intensities of projection images is used to obtain preliminary classification results through spectral clustering. In the second stage, another novel distance measure integrating the first novel distance measure and class averages generated from each group of projection images is used to obtain the final classification results through spectral clustering. The proposed two-stage spectral clustering algorithm is applied on a simulated and a real cryo-EM dataset for heterogeneous reconstruction. Results show that the two novel distance measures can be used to improve the classification performance of spectral clustering, and using the proposed two-stage spectral clustering algorithm can achieve higher classification and reconstruction accuracy than using RELION and XMIPP.

An accurate partial shading detection and global maximum power point tracking technique based on image processing

This paper aims to detect the existence of partial shading conditions on the PV array and to estimate the vicinity of the global maximum power point for shaded PV modules based solely on the captured image of the PV modules. Detecting the existence of partial shading is based on comparing the average image pixel intensity for each PV module to find any mismatch in the incident irradiance. Estimating the incident irradiance level of each PV module is based on the camera response function with the help of a reference module. Furthermore, after estimating the irradiance intensity on the modules, we used the captured image for each PV module to detect the shaded area percentage. Detect the presence of partial shading conditions and estimating the position of the Global Maximum Power Point under partial shading was achieved with simple and cheap procedure yet effective at various shading patterns regardless of the environmental circumstances.

Tumor Categorization Model (TCM) Using Soft Computing Techniques for Providing Efficient Medical Support in Brain Tumor Treatments

Brain cancer identification and segmentation is a prolonged and difficult task in Medical Image Processing, which is most significant for providing appropriate treatment and increase patient’s life span. With the advancements available in medical fields, soft computing techniques are incorporated to accurate detection and classification of brain tumors. Besides brain cancer detection, it is vital to categorize tumor stage based on their features. For that concern, this paper develops a Tumor Categorization Model (TCM) that includes image processing and soft computing techniques. Here, pre-processing is carried out using modified Gabor filter and segmentation process is performed with OTSU thresholding. Following segmentation, region growing is processed based on the pixel intensities of input MRI brain images. Further, Discrete Wavelet Transform is enforced for extorting image features as well as gray-level co-occurence matrix features are also derived for appropriate classifications. Finally, the input MRI images are classified using Boosting Support Vector Machine (BSVM) with the benchmark dataset called DICOM and BraTS dataset. The experimental results demonstrate accurate brain tumor detection and categorization by the efficient incorporation of image processing and soft computing methodologies, provides efficient clinical support in providing treatments.

An Image Encryption Algorithm Using Logistic Map with Plaintext-Related Parameter Values.

This paper deals with a plaintext-related image encryption algorithm that modifies the parameter values used by the logistic map according to plain image pixel intensities. The parameter values are altered in a row-wise manner, which enables the usage of the same procedure also during the decryption. Furthermore, the parameter modification technique takes into account knowledge about the logistic map, its fixed points and possible periodic cycles. Since the resulting interval of parameter values achieves high positive values of Lyapunov exponents, the chaotic behavior of the logistic map should be most pronounced. These assumptions are verified by a set of experiments and the obtained numerical values are compared with those reported in relevant papers. It is found that the proposed design that uses a simpler, but well-studied, chaotic map with mitigated issues obtains results comparable with algorithms that use more complex chaotic systems. Moreover, the proposed solution is much faster than other approaches with a similar purpose.

A study of real-world micrograph data quality and machine learning model robustness

Machine-learning (ML) techniques hold the potential of enabling efficient quantitative micrograph analysis, but the robustness of ML models with respect to real-world micrograph quality variations has not been carefully evaluated. We collected thousands of scanning electron microscopy (SEM) micrographs for molecular solid materials, in which image pixel intensities vary due to both the microstructure content and microscope instrument conditions. We then built ML models to predict the ultimate compressive strength (UCS) of consolidated molecular solids, by encoding micrographs with different image feature descriptors and training a random forest regressor, and by training an end-to-end deep-learning (DL) model. Results show that instrument-induced pixel intensity signals can affect ML model predictions in a consistently negative way. As a remedy, we explored intensity normalization techniques. It is seen that intensity normalization helps to improve micrograph data quality and ML model robustness, but microscope-induced intensity variations can be difficult to eliminate.

Uncertainty measurement of radiomics features against inherent quantum noise in computed tomography imaging.

Quantum noise is a random process in X-ray-based imaging systems. We addressed and measured the uncertainty of radiomics features against this quantum noise in computed tomography (CT) images. A clinical multi-detector CT scanner, two homogeneous phantom sets, and four heterogeneous samples were used. A solid tumor tissue removed from a male BALB/c mouse was included. We the placed phantom sets on the CT scanning table and repeated 20 acquisitions with identical imaging settings. Regions of interest were delineated for feature extraction. Statistical quantities-average, standard deviation, and percentage uncertainty-were calculated from these 20 repeated scans. Percentage uncertainty was used to measure and quantify feature stability against quantum noise. Twelve radiomics features were measured. Random noise was added to study the robustness of machine learning classifiers against feature uncertainty. We found the ranges of percentage uncertainties from homogeneous soft tissue phantoms, homogeneous bone phantoms, and solid tumor tissue to be 0.01-2138%, 0.02-15%, and 0.18-16%, respectively. Overall, it was found that the CT features ShortRunHighGrayLevelEmpha (SRHGE) (0.01-0.18%), ShortRunLowGrayLevelEmpha (SRLGE) (0.01-0.41%), LowGrayLevelRunEmpha (LGRE) (0.01-0.39%), and LongRunLowGrayLevelEmpha (LRLGE) (0.02-0.66%) were the most stable features against the inherent quantum noise. The most unstable features were cluster shade (1-2138%) and max probability (1-16%). The impact of random noise to the prediction accuracy by different machine learning classifiers was found to be between 0 and 12%. Twelve features were used for uncertainty measurements. The upper and lower bounds of percentage uncertainties were determined. The quantum noise effect on machine learning classifiers is model dependent. • Quantum noise is a random process and is intrinsic to X-ray-based imaging systems. This inherent quantum noise creates unpredictable fluctuations in the gray-level intensities of image pixels. Extra cautions and further validations are strongly recommended when unstable radiomics features are selected by a predictive model for disease classification or treatment outcome prognosis. • We addressed and used the statistical quantity of percentage uncertainty to measure the uncertainty of radiomics features against the inherent quantum noise in computed tomography (CT) images. • A clinical multi-detector CT scanner, two homogeneous phantom sets, and four heterogeneous samples were used in the stability measurement. A solid tumor tissue removed from a male BALB/c mouse was included in the heterogeneous sample.

The prognostic model for identification of the universe rotation axis upon applying the system of coordinates of a chosen galaxy

This paper describes the theoretical and practical preconditions for identification of the Universe rotation axis using the digital images from ten galaxies and the relative velocities of their movement upon applying a simulation of the parameter <mml:math display="inline"> <mml:mi>z</mml:mi> </mml:math> from the Doppler effect formula and a system of polar coordinates. The value of the Doppler parameter <mml:math display="inline"> <mml:mi>z</mml:mi> </mml:math> shows an approximate relative velocity of the reciprocal movement of two digital images (objects) in respect of the speed of light in vacuum c. The values of the Doppler parameter <mml:math display="inline"> <mml:mi>z</mml:mi> </mml:math> were calculated upon using vectors transformed by pixel intensities of digital images of galaxies cluster and the white solar spectrum. The average values of the Doppler parameter <mml:math display="inline"> <mml:mi>z</mml:mi> </mml:math> were calculated according to the created formula upon applying the expressions of cross-covariance functions of the algebraic sum of the relevant transformed vectors and a single vector. The values of the parameter <mml:math display="inline"> <mml:mi>z</mml:mi> </mml:math> from the Doppler formula enabled calculating the relative velocity of movement of digital images of the galaxies cluster in respect of each other and the relative velocity of movement of the images in respect of the Solar system. The relative velocity of galaxy movement calculated in such a way is a component of the real relative galaxy movement velocity (the velocity of tangential motion in respect of the rotation curve). In addition, the values of the scalar argument <mml:math display="inline"> <mml:mi>β</mml:mi> </mml:math> (the values of the angles between vectors of respective directions of vectors—directions of vectors of galaxies movement in respect of the Solar system and direction of the real galaxies movement vectors) are calculated. In the systems of polar and rectangular coordinates of each galaxy, the values of radii R of galaxies rotation curves (at points of the tangent) and coordinates of their centers were established upon applying the relevant formulas of differential geometry. The calculation data were collected using the software upon applying matlab program package operators.