Color Channels Research Articles (Page 1)

Abstract Polarization control plasmonic nanostructures provide a unique route to manipulate light–matter interactions at the nanoscale and are particularly powerful for information security applications, where polarization-encoded color images can be used for optical encryption and anticounterfeiting. Conventional plasmonic materials such as Au and Ag, however, suffer from poor thermal stability, limiting their integration into robust, CMOS-compatible devices. Here, we present a polarization-encoded color image platform based on refractory HfN plasmonic metasurfaces, which combine gold-like optical properties with exceptional hardness, compositional tunability, and superior high-temperature resilience. Periodically patterned HfN nanoantennas with widths of 200 nm exhibit well-defined localized surface plasmon resonances in the visible spectrum (628 and 564 nm) and can be selectively excited by orthogonal linear polarizations. We designed and realized a polarization-encoded color image in which distinct color channels are revealed under x- and y-polarized illumination, enabling decryption of hidden information. Under unpolarized illumination, the superposition of color channels effectively conceals the message, achieving robust optical encryption. Our results establish HfN plasmonic nanostructures as a key material platform for next-generation nanophotonics, uniquely combining gold-like optical properties with exceptional thermal robustness. Even after high-temperature annealing, HfN retains its plasmonic response, enabling reliable polarization-resolved color image encoding and decryption. This breakthrough paves the way for thermally resilient metasurfaces for secure data encryption, anticounterfeiting, and robust operation in extreme environments.

This article presents a novel augmented image encryption algorithm tailored for securing satellite images, addressing the critical need for robust protection of sensitive geographic data. Implementing Shannon’s principles of confusion and diffusion, the method begins by augmenting multiple plain images into a single large image, followed by a three-stage encryption process. Initially, the augmented image is separated into its three color channels, which are transformed into one-dimensional (1D) bit-streams, split and altered using the Gauss Circle map, and restructured via Fredkin Gates to enhance unpredictability. Subsequently, the bit-streams are converted into 1D bytes and 2 times 2 matrices, processed through three systems incorporating hyperchaos-induced keys and dynamic Hill Cipher matrices for additional confusion and diffusion. The final stage combines these encrypted streams into one image while preserving the integrity of color data. The proposed method achieves strong security metrics, including an average Number of Pixels Change Rate (NPCR) of 99.6115%, a Unified Average Changing Intensity (UACI) of 31.71%, and high entropy values (e.g., 7.9989) for encrypted images, ensuring robust resistance to differential and statistical attacks. The encryption demonstrates computational efficiency with an encryption time of 0.2817s for 256times 256 images and maintains a low Peak Signal-to-Noise Ratio (PSNR) of 8.1 dB, reflecting effective data obfuscation. This multistage chaos-based approach, leveraging Fredkin logic gates and hyperchaos-induced keys, significantly enhances security, scalability, and efficiency, making it ideal for high-stakes satellite imagery applications where data integrity and confidentiality are paramount.

Appearance is one of the important traits in seeds. Appearance-related features such as shape, size, and color are important parameters in distinguishing seeds from each other. Machine learning algorithms are used to distinguishing plant seed species for different purposes. In this study, four faba bean cultivars (Alexia, Alice, Jasmin, and Arabella) were used to distinguishing based on appearance measurements including shape and size features analyzed in pairs. Eleven machine learning algorithms (NB, MLP, SGD, SL, LMT, SMO, kNN, J48, Random Forest, Random Tree, REPTree) were used to assess binary classification performance utilizing red-green-blue (RGB) color channels through a image processing system. Among all pairs, faba bean seeds of the Alexia and Alice cultivars had the greatest classification accuracy of 90.5% using the Random Forest, and 87.5% with the MLP, SGD, and J48 models. The MLP model achieved the highest accuracy rate of 87% for the categorization of Alexia vs Arabella cultivars, followed by the J48 model with an accuracy rate of 84%. The Alice cultivar possesses the greatest values for area (83.80 mm²), perimeter (47.43 mm), width (9.28 mm), and length (12.50 mm). Wilks' lambda results indicated that the variations in external appearance of faba bean varieties are significant (p < 0.01). All of these results indicated that machine learning algorithms can effectively differentiate faba bean seeds based on their physical characteristics.

In this work, the response of peeled-off Gafchromic EBT2 film to argon plasma jet (APJ) was investigated. Peeled-off Gafchromic EBT2 films were exposed to APJ for different durations of varying steps from 0 to 70s at ambient conditions. Thereafter, the films were scanned with a flatbed scanner of high spatial resolution and color depth of 16 bits per color channel. The optical properties were measured using a UV-Vis spectrophotometer. The induced chemical modification in the active layer was confirmed by ATR-FTIR spectroscopy. The pixel values correlation of each color channel of peeled-off Gafchromic EBT2 film and the exposure time by APJ at different durations were studied. The obtained results showed that the pixel values of the red and green color channels of the peeled-off Gafchromic EBT2 film exponentially decrease with increasing the exposure time to APJ, with decay constants of 0.010 ± 0.024 and 0.071 ± 0.011 for the red and green channels, respectively. The blue channel exhibits poor and anomalous responses compared to both red and green color channels. The two characteristic peaks in the UV-Vis absorption spectra at the wavelengths of 580 ± 4nm and 632 ± 4nm for the peeled-off Gafchromic EBT2 film go up exponentially as the exposure time increases. The ATR-FTIR spectra contain two characteristic new C = C bonds; the first is formed due to a solid-state 1,4-polymerization reaction as conjugated with neighboring C ≡ C, while the second is further stabilized by conjugation to allene structure, increasing the double bond character, while the latter is the major contributor. The obtained results of the peeled-off Gafchromic EBT2 film can be utilized in APJ diagnosis and measurements.

Real-time lossless image compression based on the JPEG-LS algorithm is in high demand for critical missions such as satellite remote sensing and space exploration due to its excellent balance between complexity and compression rate. However, few researchers have made appropriate modifications to the JPEG-LS algorithm to make it more suitable for high-speed hardware implementation and application to Bayer pattern data. This paper addresses the current limitations by proposing a real-time lossless compression system specifically tailored for Bayer pattern images from spaceborne cameras. The system integrates a hybrid encoding strategy modified from JPEG-LS, combining run-length encoding, predictive encoding, and a non-encoding mode to facilitate high-speed hardware implementation. Images are processed in tiles, with each tile’s color channels processed independently to preserve individual channel characteristics. Moreover, potential error propagation is confined within a single tile. To enhance throughput, the compression algorithm operates within a 20-stage pipeline architecture. Duplication of computation units and the introduction of key-value registers and a bypass mechanism resolve structural and data dependency hazards within the pipeline. A reorder architecture prevents pipeline blocking, further optimizing system throughput. The proposed architecture is implemented on a XILINX XC7Z045-2FFG900C SoC (Xilinx, Inc., San Jose, CA, USA) and achieves a maximum throughput of up to 346.41 MPixel/s, making it the fastest architecture reported in the literature.

Image processing workflows typically consist of a series of different functions, each working with “image” inputs and outputs in an abstract sense. However, the specific in-memory representation of images differs between and sometimes within libraries. Conversion is therefore necessary when integrating functions from several sources into a single program. The conversion process forces users to consider low-level implementation details, including data types, color channels, channel order, minibatch layout, memory locations, and pixel intensity ranges. Specifically in the case of visual programming languages (VPLs), this distracts from high-level operations. We introduce im2im, a Python library that automates the conversion of in-memory image representations. The central concept of this library is a knowledge graph that describes image representations and how to convert between them. The system queries this knowledge graph to generate the conversion code and execute it, converting an image to the desired representation. The effectiveness of the approach is evaluated through two case studies in VPLs. In each case, we compared a workflow created in a basic block-based VPL with the same workflow enhanced using im2im. These evaluations show that im2im automates type conversions and eliminates the need for manual intervention. Additionally, we compared the overhead of using explicit intermediate representations versus im2im, both of which avoid manual type conversions in VPLs. The results indicate that im2im generates only the necessary conversions, avoiding the runtime overhead associated with converting to and from intermediate formats. A performance comparison between the step-by-step approach used by im2im and a single-function approach demonstrates that the overhead introduced by im2im does not impact practical usability. While focused on block-based VPLs, im2im can be generalized to other VPLs and textual programming environments. Its principles are also applicable to domains other than images. The source code and analyses are available via GitHub.

Objective. Orientation dependency of radiochromic film dosimeters has been connected to their ability to modulate the polarization of the incident light during the film scanning. We investigate the dose-dependent and color-dependent polarization properties of EBT3, EBT4, EBT-XD, MD-V3, and HD-V2 radiochromic films.Approach. Film samples were irradiated at different doses by a 6 MV photon beam. The films were scanned at 16 orientations by a flatbed scanner. The pixel value (PV) of the scanned images at each orientation and color channel (red, green, and blue) was measured. The modulation of PVs, defined as the ratio of PVmin/PVmax, was calculated for each model and dose level.Main results. In EBT3, EBT4, EBT-XD, and MD-V3 models, a cosinusoidal curve was fitted to the measured PVs in accordance with Malus's law indicating that an ensemble polarization axis exists in the films because of the elongation of the polymer chains in the active layer of the films. The modulation of PVs showed an increasing trend with dose in all film models indicating that the cross-linking and polymerization strengthened the polarization properties of the films.Significance. Our results provide valuable information about the nature of the orientation dependency of radiochromic films. The impact of orientation dependency is non negligible and to minimize any systematic inaccuracies, one must maintain the polarization between calibration and measurement film.

ABSTRACT We suggest colorimetric method based on the processing of sensor images to determine the inorganic chlorides in crude oil and water-oil emulsions. The transparent polymethacrylate sensor combines the functions of generating a colour and concentrating chloride anions several times compared to their content in oil. The colour change from red-violet to light orange is based on the destruction of the coloured complex of Hg(II) with diphenylcarbazone in the presence of chloride anions. The sensor complies with the principles of ‘green chemistry’ since the mercury cation remains within the polymer volume and does not enter the environment. Inorganic chloride is extracted into the sensor volume, followed by colorimetric quantification using digital image analysis. Digital images of the sensor are converted into red, green and blue (RGB) colour channels using a smartphone, and an image processing algorithm calculates the average RGB colour coordinates. The colorimetric method allows for the determination of inorganic chloride anions in the range of 0.2–340 mg·kg−1 and a detection limit of 0.1 mg·kg−1.

Accurate frost detection on leaf surfaces is critical for agricultural monitoring, yet existing methods struggle with segmentation errors caused by complex backgrounds (blurred, soil, weeds) and subtle frost-leaf texture differences. To address this, we propose MCGE-Frost, a multi-component gradient enhancement method that integrates color space analysis with gradient fusion theory. The algorithm extracts gradient features from individual color channels (HSV, Lab), applies adaptive weighting to enhance frost-leaf boundary contrast, and employs morphological filtering to suppress background noise. Experiments on leaf images demonstrate that MCGE-Frost achieves a total algorithmic error segmentation rate of 3.29%, significantly outperforming ExG (8.63%), OTSU (8.98%), and HSV (11.98%). The method reduces computational complexity by 40% compared to deep learning-based approaches while maintaining robustness across diverse backgrounds. MCGE-Frost achieves 0.8 s/image processing on GPU-accelerated systems, balancing accuracy and efficiency for edge deployment. Additionally, it improves the intelligence of frost quantification with minor manual calibration. This advancement supports real-time frost monitoring in precision agriculture, providing actionable insights for frost protection and crop management.

Overcoming visual degradation in challenging imaging scenarios is essential for accurate scene understanding. Although deep learning methods have integrated various perceptual capabilities and achieved remarkable progress, their high computational cost limits practical deployment under resource-constrained conditions. Moreover, when confronted with diverse degradation types, existing methods often fail to effectively model the inconsistent attenuation across color channels and spatial regions. To tackle these challenges, we propose DWMamba, a degradation-aware and weight-efficient Mamba network for image quality enhancement. Specifically, DWMamba introduces an Adaptive State Space Module (ASSM) that employs a dual-stream channel monitoring mechanism and a soft fusion strategy to capture global dependencies. With linear computational complexity, ASSM strengthens the models ability to address non-uniform degradations. In addition, by leveraging explicit edge priors and region partitioning as guidance, we design a Structure-guided Residual Fusion (SGRF) module to selectively fuse shallow and deep features, thereby restoring degraded details and enhancing low-light textures. Extensive experiments demonstrate that the proposed network delivers superior qualitative and quantitative performance, with strong generalization to diverse extreme lighting conditions. The code is available at https://github.com/WindySprint/DWMamba.

Abstract A color image encryption algorithm based on a novel 5D memristor chaotic system is proposed to prevent image information leakage during transmission. Firstly, By adding two memristors, Chua's circuit is improved to a 5D memristor chaotic system. The systematic examination of the bifurcation diagram, Lyapunov exponent spectrum, and Poincaré section indicates that the system displays chaotic dynamics. Secondly, based on the intermittent control strategy, a Predefined-time Synchronization Control (PTSC) scheme is designed for the 5D chaotic system, which can realize fast and stable synchronization control. Thirdly, by using the above chaotic sequences, a cross-plane scrambling technique is employed to destroy the correlation. Subsequently, by combining this technology with DNA coding, a color image encryption scheme is designed. Finally, security analysis and comparative experiments demonstrate that the proposed encryption strategy can effectively weaken the correlation between pixels across different color channels, while resisting noise and cropping attacks.

Investigating the Spatial Resolution and Contrast of Peeled-Off Gafchromic EBT2 Film Exposed to 5 MeV Alpha Particles Analyzed via Color Channels

Algorithmic Computability of the Capacity of Additive Colored Gaussian Noise Channels

We investigated how early human visual cortex processes color by analyzing individual variability in steady-state visual evoked potentials (SSVEPs). Sixteen participants viewed a flickering checkerboard that swept around the isoluminant hue circle at three chromatic contrasts. The current study analyzed the individual variability in the SSVEP data from the study to elucidate the hue-selective mechanisms in the early visual areas using a factor-analytic approach. The initial analyses of the correlations revealed that the responses to the nearby hues correlated highly, which is consistent with multiple overlapping color channels. Also, the correlational pattern showed consistent peaks and troughs at specific hue angles: 0° (+L–M), 30°, 120°, 180° (−L+M), 240°, and 300°. We further performed nonmetric multidimensional scaling, identifying four significant hue dimensions. Peaks and troughs of the dimension components were consistent with the hue angles revealed in the correlational pattern. Additional four hues also appeared in the last dimension: 90° (+S), 150°, 270° (−S), and 330°. The 10 (six plus four) hues suggested in these analyses may subserve the basis of early cortical color processing, including classical cone opponency and the mechanisms tuned to the intermediate hues.

We investigate the optical response of a planar nematic liquid crystal (LC) cell under varying electric fields and polarizer orientations using a combination of Monte Carlo (MC) simulations and Mueller matrix formalism. The LC molecular configurations are generated using a Lebwohl–Lasher-type lattice model with periodic boundary conditions, electric field coupling, and surface anchoring interactions. These configurations are incorporated into a Mueller matrix framework to calculate the spectrally dependent transmittance through a crossed-polarizer setup for three primary wavelengths: red (λR=700 nm), green (λG=546.1 nm), and blue (λB=435.8 nm), corresponding to simply the RGB color channels, respectively. By systematically varying the polarizer azimuthal angle (𝛼=0⁰, -22.5⁰, and -45⁰), we demonstrate that both the transmitted intensities and the resulting color maps are strongly modulated by changes in electric field and crossed polarizer’s azimuthal angle. To visualize these effects, RGB-based color maps are constructed, providing an intuitive representation of the optical response as a function of system parameters. The results reveal a strong dependence of output intensity and color on the LC molecular orientation, confirming the capability of this simulation-based approach for designing tunable LC optical elements and display technologies.

Dynamic optical coherence tomography (dOCT) uses signal fluctuations to contrast different cells and tissues. In this paper, we demonstrate that shortening the time base over which the signal fluctuations are evaluated reduces noise induced by motion while still maintaining a decent image quality. Automatic clustering using the neural-gas algorithm is introduced to optimize the border between the color channels. The performance of the automatic border optimization is demonstrated with 15 different tissue samples by quantitative assessment of motion-induced noise and image quality using the mean squared error (MSE) between images and the image quality parameters peak signal to noise ratio (PSNR) and structural similarity (SSIM).

The Ising model is important in statistical modeling and inference in many applications, however its normalizing constant, mean number of active vertices and mean spin interaction – quantities needed in inference – are computationally intractable. We provide accurate approximations that make it possible to numerically calculate these quantities in the homogeneous case. Simulation studies indicate good performance of our approximation formulae that are scalable and unfazed by the size (number of nodes, degree of graph) of the Markov Random Field. The practical import of our approximation formulae is illustrated in performing Bayesian inference in a functional Magnetic Resonance Imaging activation detection experiment, in likelihood ratio testing, for anisotropy in the spatial patterns of yearly increases in pistachio tree yields, and for independence of the least significant bit in the three color channels of a gigapixel image.

This study presents the formalization of mathematical models of hardware-optical distortions in digital images captured during aerial photography from onboard systems of Unmanned Aerial Vehicles (UAVs). These distortions significantly affect the accuracy and reliability of automated object detection and classification algorithms in complex outdoor environments. A generalized classification scheme of distortions is proposed, accounting for their origins and dividing image degradation into hardware- optical, dynamic, and environmental factors that induce structural instability in the input data. The research formulates mathematical modeling tasks for key types of hardware-optical distortions, including: spherical aberration is formalized through a spatially-dependent point spread function (PSF); chromatic aberration is described as linear displacement of additive color channels as a function of radial distance from the image center; geometric distortion is modeled by radial coordinate transformation using calibrated lens parameters; defocus blur is represented by a spatially-variant Gaussian blur kernel incorporating local scene depth; and sensor noise is modeled as a combination of stochastic processes using normal and Poisson distributions. The paper substantiates the selection of these mathematical models as a foundation for generating synthetic image datasets in the training of deep learning neural architectures, with the goal of enhancing robustness to real-world distortions. A comparative analysis is performed to assess the impact of each distortion type on image quality, information loss, and suitability for further processing-particularly at the stages of segmentation, object detection, and classification under conditions such as variable backgrounds, partial occlusion, or low illumination. Methodological recommendations are developed for generating training datasets with defined levels of complexity and distortion, reflecting the real-world conditions of UAV imaging systems, including varying natural lighting, platform instability, vibration, atmospheric scattering, and design limitations of compact sensors. The study concludes that the use of adaptively generated datasets with a priori modeled distortions significantly improves the robustness, accuracy, and generalization capability of modern neural network models, especially in practical deployments of UAV platforms along active confrontation lines.

Traditional LED display systems employing RGB three-channel structures face significant limitations in color gamut coverage due to their triangular primary color configurations, resulting in color deviation, transition distortion, and color compression issues that cannot meet the stringent visual fidelity requirements of emerging applications. This research proposes extending the input from traditional RGB to a four-channel RGBV structure incorporating luminance information, while introducing a five-channel emission mechanism RGBCX at the output end. By inserting auxiliary channels C (cyan) and X (yellow) between green-blue and red-green regions, this approach effectively covers mixed color regions outside the RGB triangle. We construct a 4 × 5 mapping matrix W and design a differentiable optimization objective function that jointly considers color fidelity (CIEDE2000 color difference), color gamut coverage (based on five-primary convex hull area), channel balance (variance minimization), energy conservation, and non-negativity constraints. A staged optimization algorithm framework combining non-negative least squares (NNLS) and regularized gradient descent is proposed to efficiently solve the multi-objective mapping weight matrix. This approach addresses the critical demands of ultra-high-definition video (UHD), augmented reality (AR), virtual reality (VR), and human-computer interaction applications for enhanced color vividness and spatial consistency. Experimental validation demonstrates that CEMMO achieves 23.6% improvement in color reproduction accuracy, 19.4% increase in color gamut volume, while reducing energy consumption by 5.8%. The proposed methodology establishes an efficient transformation framework from four-channel input signals to five-channel display outputs.

Laser-induced breakdown spectroscopy for imaging and distribution analysis of heavy metal elements in soil.