RGB Channels Research Articles

Quantum color image watermarking scheme based on quantum discrete cosine transform

Quantum watermarking is a technique that embeds specific information into a quantum carrier, used for digital copyright protection. This paper introduces a new quantum color image watermarking method that combines Quantum LOG (QLOG) edge detection with Quantum Discrete Cosine Transform (QDCT) technology. First, edge detection is used on the RGB channels of the quantum color image. This helps identify the best channel for embedding the watermark. Next, the image is decomposed using the quantum Haar wavelet transform (QHWT). The image is then processed with QDCT, selecting the mid-frequency parts for watermark embedding. Using QDCT improves the watermark’s resistance to JPEG compression. The invisibility of the watermark is verified by assessing its Peak Signal-to-Noise Ratio (PSNR) and histogram. Comparative analysis shows strong resistance to various noise attacks. Simulation results confirm that the watermarking technique maintains high visual quality and resists different adversarial attacks.

RBFNN-PSO Intelligent Synchronisation Method for Sprott B Chaotic Systems with External Noise and Its Application in an Image Encryption System.

In the past two decades, research in the field of chaotic synchronization has attracted extensive attention from scholars, and at the same time, more synchronization methods, such as chaotic master-slave synchronization, projection synchronization, sliding film synchronization, fractional-order synchronization and so on, have been proposed and applied to chaotic secure communication. In this paper, based on radial basis function neural network theory and the particle swarm optimisation algorithm, the RBFNN-PSO synchronisation method is proposed for the Sprott B chaotic system with external noise. The RBFNN controller is constructed, and its parameters are used as the particle swarm particle optimisation parameters, and the optimal values of the controller parameters are obtained by the PSO training method, which overcomes the influence of external noise and achieves the synchronisation of the master-slave system. Then, it is shown by numerical simulation and analysis that the scheme has a good performance against external noise. Because the Sprott B system has multiple chaotic attractors with richer dynamics, the synchronization system based on Sprott B chaos is applied to the image encryption system. In particular, the Zigzag disambiguation method for top corner rotation and RGB channel selection is proposed, and the master-slave chaotic system synchronisation sequences are diffused to the disambiguated data streams, respectively. Therefore, the encryption and decryption of image transmission are implemented and the numerical simulation results are given, the random distribution characteristics of encrypted images are analysed using histogram and Shannon entropy methods, and the final results achieve the expected results.

Paper-based isotachophoretic preconcentration technique for low-cost determination of glyphosate.

Electrophoretic microfluidic paper-based analytical devices (e-µPADs) are promising for low-cost and portable technologies, but quantitative detection remains challenging. In this study, we develop a paper-based isotachophoretic preconcentration and separation method for the herbicide glyphosate as a model analyte. The device, consisting of two electrode chambers filled with leading and terminating electrolytes and a nitrocellulose strip as the separation carrier, was illuminated by a flat light source and operated with a voltage supply of 400V. Detection was accomplished using a simple camera. Colorimetric detection was optimized through competitive complexation between glyphosate, copper ions, and pyrocatechol violet as a dye. The buffer system was optimized using simulations, (i) ensuring the pH was optimal for the demetallation of the blue pyrocatechol violet-copper complex [PV] to the yellow free dye and (ii) ensuring the electrophoretic migration of glyphosate into the slower [PV] for the colorimetric reaction. A new data evaluation method is presented, analyzing the RGB channel intensities. The linear range was between 0.8 and 25µM, with a LOD of approximately 0.8µM. The ITP separation preconcentrated glyphosate by a factor of 820 in numerical simulations. The method may be applied to control glyphosate formulations, especially in developing countries where herbicide sales and applications are poorly regulated.

Stegocrypt: A robust tri‐stage spatial steganography algorithm using TLM encryption and DNA coding for securing digital images

AbstractThis research work presents a novel secured spatial steganography algorithm consisting of three stages. In the first stage, a secret message is divided into three parts, each is encrypted using a tan logistic map encryption key with a unique seed value. In the second stage, the encrypted parts are transformed into quick response codes, serving as a layer of channel coding. Subsequently, the quick response codes are decoded back into bit‐streams. To enhance security, a uniquely‐seeded Mersenne Twister key is generated and employed to apply DNA coding onto each bit‐stream. The resulting bit‐streams are then embedded in the least significant bits of the RGB channels of a cover image. Finally, the RGB channels are merged to form a single stego image. A comprehensive set of experimental analyses is conducted to evaluate the performance of the proposed secure steganography algorithm. The experimental results demonstrate the algorithm's robustness against various attacks and its ability to achieve high embedding capacity while maintaining imperceptibility. The proposed algorithm offers a promising solution for secure information hiding in the spatial domain, with potential applications in areas such as data transmission, digital forensics, and covert communication.

Determination of Cortisol Hormone from Sweat Samples and Interpretation with Microcontroller

Cortisol, known as the body's stress hormone, regulates metabolism, inflammation, and the immune system. It affects various bodily systems such as the nervous, immune, cardiovascular, respiratory, reproductive, and musculoskeletal systems. Normal sweat cortisol levels range from 8 to 142 ng/ml, with elevated levels indicating stress (200 ng/ml in women, 500 ng/ml in men). This study focuses on colorimetric cortisol analysis using a microfluidic layer to collect sweat samples. Samples are mixed with a specific color reagent. Cortisol levels are quantitatively analyzed using Z-Score values and RGB channel density via photographs taken with the ESP 32 module. The aim is to determine cortisol levels, aiding in diagnosing conditions like Cushing's syndrome and Addison's Disease, leveraging wearable technology. The developed system integrates a microcontroller into the wearable microfluidic system for cortisol measurements throughout the day. Data is displayed and recorded on a computer for streamlined cortisol monitoring. The blue tetrazolium method used offers stable colors and rapid reactions (10 minutes). The Limit of Detection (LOD) is 0.3 ug/mL, with a detection limit of 1.1 ug/mL. Combined with the ESP 32 Cam module, the system detects cortisol concentrations from 0.8 – 60 ug/mL.

End-to-end color fringe depth estimation based on a three-branch U-net network

In fringe projection profilometry (FPP), end-to-end depth estimation from fringe patterns for FPP attracts more and more attention from fringe patterns. However, color images provide additional information from the RGB channel for FPP, which has been paid little attention in depth estimation. To this end, in this paper we present for the first time, to the best of our knowledge, an end-to-end network for depth estimation using color composite fringes with better performance. In order to take advantage of the color fringe pattern, a multi-branch structure is designed in this paper, which learns the multi-channel details of the object under test by using three encoders for each RGB channel and introduces an attention module to better capture the complex features and modalities information in the input data. Experiments from simulated and real datasets show that the proposed method with color fringe pattern is effective for depth estimation, and it outperforms other deep learning methods such as UNet, R2Unet, PCTNet, and DNCNN.

Lossy Image Compression with Stochastic Quantization

Introduction. Lossy image compression algorithms play a crucial role in various domains, including graphics, and image processing. As image information density increases, so do the resources required for processing and transmission. One of the most prominent approaches to address this challenge is color quantization, proposed by Orchard et al. (1991). This technique optimally maps each pixel of an image to a color from a limited palette, maintaining image resolution while significantly reducing information content. Color quantization can be interpreted as a clustering problem (Krishna et al. (1997), Wan (2019)), where image pixels are represented in a three-dimensional space, with each axis corresponding to the intensity of an RGB channel. The purpose of the paper. Scaling of traditional algorithms like K-Means can be challenging for large data, such as modern images with millions of colors. This paper reframes color quantization as a three-dimensional stochastic transportation problem between the set of image pixels and an optimal color palette, where the number of colors is a predefined hyperparameter. We employ Stochastic Quantization (SQ) with a seeding technique proposed by Arthur et al. (2007) to enhance the scalability of color quantization. This method introduces a probabilistic element to the quantization process, potentially improving efficiency and adaptability to diverse image characteristics. Results. To demonstrate the efficiency of our approach, we present experimental results using images from the ImageNet dataset. These experiments illustrate the performance of our Stochastic Quantization method in terms of compression quality, computational efficiency, and scalability compared to traditional color quantization techniques. Conclusions. This study introduces a scalable algorithm for solving the color quantization problem without memory constraints, demonstrating its efficiency on a subset of images from the ImageNet dataset. The convergence speed of the algorithm can be further enhanced by modifying the update rule with alternative methods to Stochastic Gradient Descent (SGD) that incorporate adaptive learning rates. Moreover, the stochastic nature of the proposed solution enables the utilization of parallelization techniques to simultaneously update the positions of multiple quants, potentially leading to significant performance improvements. This aspect of parallelization and its impact on algorithm efficiency presents a topic for future research. The proposed method not only addresses the limitations of existing color quantization techniques but also opens up new possibilities for optimizing image compression algorithms in resource-constrained environments. Keywords: non-convex optimization, stochastic optimization, stochastic quantization, color quantization, lossy compression.

Quaternion Nuclear Norm Minus Frobenius Norm Minimization for color image reconstruction

Color image restoration methods typically represent images as vectors in Euclidean space or combinations of three monochrome channels. However, they often overlook the correlation between these channels, leading to color distortion and artifacts in the reconstructed image. To address this, we present Quaternion Nuclear Norm Minus Frobenius Norm Minimization (QNMF), a novel approach for color image reconstruction. QNMF utilizes quaternion algebra to capture the relationships among RGB channels comprehensively. By employing a regularization technique that involves nuclear norm minus Frobenius norm, QNMF approximates the underlying low-rank structure of quaternion-encoded color images. Theoretical proofs are provided to ensure the method’s mathematical integrity. Demonstrating versatility and efficacy, the QNMF regularizer excels in various color low-level vision tasks, including denoising, deblurring, inpainting, and random impulse noise removal, achieving state-of-the-art results.

Underwater Image Enhancement Based on Luminance Reconstruction by Multi-Resolution Fusion of RGB Channels.

Underwater image enhancement technology is crucial for the human exploration and exploitation of marine resources. The visibility of underwater images is affected by visible light attenuation. This paper proposes an image reconstruction method based on the decomposition-fusion of multi-channel luminance data to enhance the visibility of underwater images. The proposed method is a single-image approach to cope with the condition that underwater paired images are difficult to obtain. The original image is first divided into its three RGB channels. To reduce artifacts and inconsistencies in the fused images, a multi-resolution fusion process based on the Laplace-Gaussian pyramid guided by a weight map is employed. Image saliency analysis and mask sharpening methods are also introduced to color-correct the fused images. The results indicate that the method presented in this paper effectively enhances the visibility of dark regions in the original image and globally improves its color, contrast, and sharpness compared to current state-of-the-art methods. Our method can enhance underwater images in engineering practice, laying the foundation for in-depth research on underwater images.

Investigating the method of selection of background pixel values for the calibration of EBT‐XD film dosimetry

AbstractPurposeThis study investigates three different calibration methods for the selection of background pixel intensity.MethodsFilm‐by‐Film (FBF) Method: Each film serves as its own control. Batch‐by‐Film (BBF) Method: A single film is used as a control for all calibration films. Generic (GEN) Method: A generic value (65535) is used as the background pixel value for all calibration films.Three calibration curves were established for the red, green, blue, and RGB channels, and the Radbard NIH (image) curve‐fitting model was used to predict the dose. Sensitivity at different dose levels was quantified by calculating the first derivative of each color channel.ResultsThe GEN method exhibited a difference of up to 6% between the predicted and delivered doses below 2 Gy. The changes in optical density when using the GEN method differed significantly (p<0.0001) from those of the FBF and BBF methods. In the dose range 5–30 Gy, the percentage difference between the predicted and delivered doses for the FBF, BBF, and GEN methods was within 2%. Both the red and green channels demonstrated higher sensitivity than the blue channel over the dose range of 2–30 Gy.ConclusionsThe FBF method is more accurate than the BBF and GEN methods because it accounts for inter‐film variations. The Radbard NIH (image) curve‐fitting function proved suitable for predicting the dose for all the three calibration methods.

Enhancing Retina Images by Lowpass Filtering Using Binomial Filter.

This study presents a method to enhance the contrast and luminosity of fundus images with boundary reflection. In this work, 100 retina images taken from online databases are utilized to test the performance of the proposed method. First, the red, green and blue channels are read and stored in separate arrays. Then, the area of the eye also called the region of interest (ROI) is located by thresholding. Next, the ratios of R to G and B to G at every pixel in the ROI are calculated and stored along with copies of the R, G and B channels. Then, the RGB channels are subjected to average filtering using a 3 × 3 mask to smoothen the RGB values of pixels, especially along the border of the ROI. In the background brightness estimation stage, the ROI of the three channels is filtered by binomial filters (BFs). This step creates a background brightness (BB) surface of the eye region by levelling the foreground objects like blood vessels, fundi, optic discs and blood spots, thus allowing the estimation of the background illumination. In the next stage, using the BB, the luminosity of the ROI is equalized so that all pixels will have the same background brightness. This is followed by a contrast adjustment of the ROI using CLAHE. Afterward, details of the adjusted green channel are enhanced using information from the adjusted red and blue channels. In the color correction stage, the intensities of pixels in the red and blue channels are adjusted according to their original ratios to the green channel before the three channels are reunited. The resulting color image resembles the original one in color distribution and tone but shows marked improvement in luminosity and contrast. The effectiveness of the approach is tested on the test images and enhancement is noticeable visually and quantitatively in greyscale and color. On average, this method manages to increase the contrast and luminosity of the images. The proposed method was implemented using MATLAB R2021b on an AMD 5900HS processor and the average execution time was less than 10 s. The performance of the filter is compared to those of two other filters and it shows better results. This technique can be a useful tool for ophthalmologists who perform diagnoses on the eyes of diabetic patients.

Enhancing reliability for AFB1 analysis in food: Ratiometric fluorescence/colorimetric dual-modal analysis platform using multifunctional GO-Fe3O4

Adsorption of DNA fluorescent probes on GO-Fe3O4 is a promising strategy for establishing fluorescent bioassays, often using magnetic separation or fluorescence quenching to generate signals. However, there is a lack of systematic understanding of ssDNA-regulated changes in the enzyme-mimetic activity of GO-Fe3O4, and the accuracy of the results of single-mode fluorescence analysis is susceptible to environmental interference. These limit the rational design and scope of application of the methods. Herein, the force and the catalytic mechanism of ssDNA/GO-Fe3O4 interactions were explored in detail. On this basis, a ratiometric fluorescence/colorimetric dual-modal analysis platform was constructed based on the superparamagnetism and DNA controllable peroxidase-like activity of GO-Fe3O4. The ratiometric fluorescent signal was generated by combining 7-amino-4-methyl-3-coumarinylacetic acid (AMCA) labeled aptamer (AMCA-aptamer) with AT hairpin-synthesized copper nanoparticles, which has built-in correction and resistance to environmental interference. The aptamer-modulated peroxidase-like activity of GO-Fe3O4 generated the colorimetric signal. Two signals correct each other to further enhance the reliability of the results. The analytical platform performed satisfactorily for AFB1 detection in the range of 0.1–150 μg/L, and was successfully applied to real samples (peanut, milk powder, and wheat flour). With the support of ImageJ software, quantitative detection was achieved by RGB channel analysis for real-color images, which provides a potential pathway for the rapid detection of food safety.

Cross-Channel Color Image Encryption Scheme Based on Discrete Memristive Coupled Neurons and DWT Compression

To address the consumption and security of color images for transmission and storage, a cross-channel color image encryption scheme based on a discrete memristive coupled neuron model and DWT compression is designed in this article. Firstly, the dynamics of the discrete memristive coupled neuron system are analyzed and found to possess the hyperchaotic phenomenon, which provides sufficient security for the encryption scheme. Secondly, the color image processed by discrete wavelet transform (DWT) has a quarter of the previous capacity. Then, the color image is combined with a Hash function, and the resulting Hash sequence is given the initial value of the hyperchaotic system. Next, a particle swarm foraging algorithm (PSFA) is designed to better disrupt the correlation in the RGB channel. Finally, a complementary DNA coding rule is implemented for the further encryption of color images. Simulation results show that even with DWT lossy compression, the recovered image can be clearly seen. The performance analysis illustrates that under the hyperchaotic system, the proposed encryption algorithm brings higher security for color images.

Design of High-Secure Digital/Optical Double Color Image Encryption Assisted by 9D Chaos and DnCNN

With the rapid development of multimedia technology, securing the transfer of images becomes an urgent matter. Therefore, designing a high-speed/secure system for color images is a real challenge. A nine-dimensional (9D) chaotic-based digital/optical encryption schem is proposed for double-color images in this paper. The scheme consists of cascaded digital and optical encryption parts. The nine chaotic sequences are grouped into three sets, where each set is responsible for encryption one of the RGB channels independently. One of them controls the fusion, XOR operation, and scrambling-based digital part. The other two sets are used for controlling the optical part by constructing two independent chaotic phase masks in the optical Fourier transforms domain. A denoising convolution neural network (DnCNN) is designed to enhance the robustness of the decrypted images against the Gaussian noise. The simulation results prove the robustness of the proposed scheme as the entropy factor reaches an average of 7.997 for the encrypted color lena-baboon images with an infinite peak signal-to-noise ratio (PSNR) for the decrypted images. The designed DnCNN operates efficiently with the proposed encryption scheme as it enhances the performance against the Gaussian noise, where the PSNR of the decrypted Lena image is enhanced from 27.01 dB to 32.56 dB after applying the DnCNN.

Face Anti-Spoofing Detection with Multi-Modal CNN Enhanced by ResNet

The growing prevalence of face recognition technology in various applications, including mobile devices, access control, and financial transactions, highlights its importance. However, the vulnerability of face recognition systems to attacks has been demonstrated, underscoring the necessity of addressing potential weaknesses that attackers may exploit. The paper delves into face presentation attack detection (PAD) within biometric systems, which is crucial for ensuring the reliability and security of face recognition algorithms. To address this issue, the paper proposes a method for face presentation attack detection using ResNet-50 in conjunction with multi-modal data, incorporating RGB, depth, infrared (IR), and thermal channels. The method explores diverse strategies to combine results from each modality, investigating various fusion techniques such as majority voting, weighted voting, average pooling, and a stacking classifier. The system has been tested on the WMCA dataset. It exhibits strong performance compared to existing methods, notably achieving an impressive ACER ratio of 0.087% with the stacking classifier. This approach proves effective by consolidating multiple modalities without requiring individual scenario-specific models, indicating promise for real-world applications

New method of colour image encryption using triple chaotic maps

AbstractA new image encryption algorithm based on the triple chaotic maps is proposed to deal with the issues of inadequate security and low encryption efficiency. Coloured images consist of three linked channels used in the scheme. This method uses different keys to break the correlations between adjacent pixels in each channel. The triple chaotic maps are Lorenz, 2D‐Logistic, and Henon. First, the plain image is split into RGB channels to encrypt each channel separately. Second, the triple chaotic maps generate two groups of keys. The first group of keys performs a pixel permutation, resulting in scrambled channels used as input for the following step. Finally, the second group of keys is used to diffuse the scrambled channels independently, resulting in diffused channels, which are then merged to obtain a cipher image. The triple chaotic maps of different orders generate the cipher image with great unpredictability and security. The security is evaluated using various measures. The results demonstrated a high level of security attained by successfully encrypting coloured images. Recent encryption algorithms are compared in terms of entropy, correlation coefficients, and attack robustness. The proposed method provided outstanding security and outperformed existing image encryption algorithms.

A novel competitive color-tone change fluorescence immunochromatographic assay for the ultrasensitive detection of pesticide and veterinary drug residues

One important challenge faced by competitive immunoassay techniques is the inability to visually distinguish weak color changes in the T-line during the detection of trace analyte concentrations. In this study, we introduced multi-color fluorescence into the immunoassay system and developed a novel competitive dual-channel color-tone change fluorescent immunochromatographic assay (CFICA) for the visual and quantitative detection of kanamycin (KAN) and carbendazim (CBZ). At the same time, we have developed matching, portable fluorescence-reading equipment, which can analyse the spectral data of the RGB channel individually and can effectively solve the problems of the traditional single-fluorescence reader – large reading error and poor detection sensitivity. After optimization, our CFICA, combined with the corresponding instrument, achieved low limits of detection (LOD) for KAN and CBZ, as low as 1.98 and 12.82 pg/mL, respectively. We believe that this CFICA detection system, with its high accuracy, low error, and multi-color detection capabilities, holds tremendous potential for visual and multi-channel competitive point-of-care testing.

Smartphone-based ratiometric fluorescence sensor for sensitive visual detection of H2O2 and glucose based on B-CDs-Ag NPRs-OPD ternary system

A straightforward and highly sensitive ratiometric fluorescence sensor comprising blue emissive carbon dots, silver nanoprisms, and o-phenylenediamine (B-CDs-Ag NPRs-OPD) was developed for the detection of H2O2 and glucose. Ag NPRs were etched into Ag+ ions upon exposure to H2O2, which caused non-fluorescent OPD to be oxidized and produce 2,3-diaminophenazine (DAP), resulting in intense yellow fluorescence emission at 555 nm. DAP can quench the B-CDs based on inner filter effect (IFE), leading to a decrease in their fluorescence intensity at 435 nm. Because glucose oxidase may catalyze the oxidation of glucose to produce H2O2, this approach can also be used to detect glucose. The unique triangular structure leads to the easy etching of the edge and tip of Ag NPRs by H2O2 into Ag+ ions, allowing for the high sensitivity of detection of H2O2 and glucose with limit of detection (LOD) of 0.12 μM and 0.6 μM, respectively. Furthermore, with the increase of the concentrations of H2O2 and glucose, a visible color shift from blue to yellow-green occurs, which can be detected using a smartphone's color recognition application that captures the RGB channel values of the image. Real-time and on-spot detection of H2O2 and glucose were achieved based on smartphone sensing platform, presenting vast potential for applications in various fields.

A method of degradation mechanism-based unsupervised remote sensing image super-resolution

Remote sensing image (RSI) super-resolution (SR) is an efficient and low-cost technique to achieve high-resolution and high-quality reconstruction images. The quality of RSI SR reconstruction is affected by the prior information contained in the degradation model. Therefore, studying how to incorporate more RSI degradation prior into the degradation model is crucial. This article presents an approach to design the degradation model by extracting degradation factors from the perspective of remote sensing imaging mechanisms. It includes two aspects: simulating the atmospheric scattering effect through RGB channel weights downsampling and the comprehensive degradation effect of the remote sensing imaging platform through combined blurring. Furthermore, we proposed a high-performance RSI SR network based on degradation mechanism (RSN-DM), which includes a degrader D and a generator G, to employ remote sensing prior fully. We conducted experiments on the UC Merced Land-Use and WPU-RESIS45 datasets, demonstrating that our proposed method is effective. Our method achieves state-of-the-art (SOTA) performance in quantitative evaluation and visual quality. Finally, we apply the proposed degradation model to other networks to further validate the model's effectiveness. Therefore, the degradation model proposed in this paper can enhance the performance of remote sensing image super-resolution techniques in practical applications.

Improving the Quality and Performance of Underwater Image Classification using the CLAHE-CNN Method

Research on underwater image analysis is critical because of challenges such as color distortion, low contrast, and noise in images. Various methods have been proposed to overcome this problem. To improve the quality and classification of underwater photos, this study aims to ensure improved performance using Contrast Limited Adaptive Histogram Equalization (CLAHE) and Convolutional Neural Networks (CNN) on underwater image datasets. The dataset consists of 500 JPG image with RGB channel and dimensions of 512 × 512 collected from online sources. There are classifications of sharks, eels, dolphins, sea rays, and whales in the underwater imagery dataset. The experiment was conducted using Python programming language on a computer that had 24GB RAM, Intel® Core™ i7-10510U CPU and hardware properties of Intel® HD Graphics graphics card. The results of this study show how CLAHE improved the CNN classification of underwater imagery by 0.91% in training data, 0.45% in validation data, and 2.02% in test data.