Color Image Research Articles

Advances in Emerging Non-Destructive Technologies for Detecting Raw Egg Freshness: A Comprehensive Review

Eggs are a rich food source of proteins, fats, vitamins, minerals, and other nutrients. However, the egg industry faces some challenges such as microbial invasion due to environmental factors, leading to damage and reduced usability. Therefore, detecting the freshness of raw eggs using various technologies, including traditional and non-destructive methods, can overcome these challenges. As the traditional methods of assessing egg freshness are often subjective and time-consuming, modern non-destructive technologies, including near-infrared (NIR) spectroscopy, Raman spectroscopy, fluorescence spectroscopy, computer vision (color imaging), hyperspectral imaging, electronic noses, and nuclear magnetic resonance, have offered objective and rapid results to address these limitations. The current review summarizes and discusses the recent advances and developments in applying non-destructive technologies for detecting raw egg freshness. Some of these technologies such as NIR spectroscopy, computer vision, and hyperspectral imaging have achieved an accuracy of more than 96% in detecting egg freshness. Therefore, this review provides an overview of the current trends in the state-of-the-art non-destructive technologies recently utilized in detecting the freshness of raw eggs. This review can contribute significantly to the field of emerging technologies in this research track and pique the interests of both food scientists and industry professionals.

Novel Steganographic Method Based on Hermitian Positive Definite Matrix and Weighted Moore–Penrose Inverses

In this paper, we describe the concept of a new data-hiding technique for steganography in RGB images where a secret message is embedded in the blue layer of specific bytes. For increasing security, bytes are chosen randomly using a random square Hermitian positive definite matrix, which is a stego-key. The proposed solution represents a very strong key since the number of variants of positive definite matrices of order 8 is huge. Implementing the proposed steganographic method consists of splitting a color image into its R, G, and B channels and implementing two segments, which take place in several phases. The first segment refers to embedding a secret message in the carrier (image or text) based on the unique absolute elements values of the Hermitian positive definite matrix. The second segment refers to extracting a hidden message based on a stego-key generated based on the Hermitian positive definite matrix elements. The objective of the data-hiding technique using a Hermitian positive definite matrix is to embed confidential or sensitive data within cover media (such as images, audio, or video) securely and imperceptibly; by doing so, the hidden data remain confidential and tamper-resistant while the cover media’s visual or auditory quality is maintained.

Elliptic Quaternion Matrices: A MATLAB Toolbox and Applications for Image Processing

In this study, we developed a MATLAB 2024a toolbox that performs advanced algebraic calculations in the algebra of elliptic numbers and elliptic quaternions. Additionally, we introduce color image processing methods, such as principal component analysis, image compression, image restoration, and watermarking, based on singular-value decomposition theory for elliptic quaternion matrices; we added these to the newly developed toolbox. The experimental results demonstrate that elliptic quaternionic methods yield better image analysis and processing performance compared to other hypercomplex number-based methods.

Prediction of Dry Mouth Condition Using Radiomics Features from Tongue Diagnosis Image

Xerostomia, commonly known as dry mouth, is characterized by reduced salivary secretion, which can lead to various oral health issues and discomfort. In this paper, we propose a novel, non-invasive method for predicting xerostomia through the analysis of tongue images. To predict salivary gland secretion from tongue images, we collected images from patients who visited the hospital with complaints of dry mouth and measured their saliva secretion. Features were extracted from these tongue images, and correlation analysis was performed using machine learning techniques to assess the relationship between the extracted features and measured saliva secretion. We obtained tongue images and saliva secretion measurements from 176 patients. Images were cropped to 100 × 100 pixels, resulting in 462 features. The dataset was divided into training and test sets, consisting of 160 and 16 samples, respectively. The correlation coefficients for the training and test datasets were 0.9496 and 0.9415, respectively, while the correlation coefficient for the entire dataset was 0.9482. The estimated linear equation was y = 0.9244x + 2.1664. This study aimed to predict salivary gland secretion based on tongue images. By extracting features from color images and employing a neural network machine learning model, we estimated salivary gland secretion. With a sufficiently large dataset of tongue images, further advancements in regression analysis using deep learning techniques could enhance the accuracy of these predictions.

A Novel Color Image Encryption Algorithm Based on Hybrid Two-Dimensional Hyperchaos and Genetic Recombination

The transition from text to images as the primary form of information transmission has recently increased the need for secure and effective encryption techniques due to the expanding information dimensions. The color picture encryption algorithm utilizing chaotic mapping is limited by a small chaotic range, unstable chaotic state, and lengthy encryption duration. This study integrates the Ackley function and the Styblinski–Tang function into a novel two-dimensional hyperchaotic map for optimization testing. A randomness test is run on the chaotic sequence created by the system to check that the new chaotic system can better sustain the chaotic state. This study introduces two techniques, genetic recombination and clock diffusion, to simultaneously disperse and mix images at the bit level. This study utilizes chaotic sequences in genetic recombination and clock drift to propose an image encryption technique. The data indicates that the method demonstrates high encryption efficiency. At the same time, the key also successfully passed the NIST randomness test, verifying its sensitivity and randomness. The algorithm’s dependability has been demonstrated and can be utilized for color image encryption.

Analysis of clinicopathological factors associate with the visibility of early gastric cancer in endoscopic examination and usefulness of linked color imaging: A multicenter prospective study.

This study investigated clinicopathological factors associated with the visibility of early gastric cancer and the efficacy of linked color imaging. Patients with early gastric cancer who underwent endoscopic treatment between April 2021 and July 2022 were enrolled. All cases underwent white light imaging and linked color imaging. Three experts evaluated lesion visibility using a visual analog scale. A mean score ≥3 on white light imaging was defined as "good visibility", and <3 as "poor visibility". We extracted patient information and endoscopic and pathological data for the lesion and background mucosa, analyzed factors associated with the visibility of early gastric cancer, and compared visibility between white light imaging and linked color imaging. Ninety-seven lesions were analyzed, with good visibility in 49 and poor visibility in 48. Multivariate analysis revealed small lesion size (odds ratio 1.89) and presence of endoscopic intestinal metaplasia (odds ratio 0.49) as significantly associated with the poor visibility of early gastric cancer. Mean visibility score was significantly higher for linked color imaging (P<0.001). Mean score for linked color imaging was significantly higher in the poor visibility group (P<0.001), but not significantly different in the good visibility group (P = 0.292). Mean score was significantly higher with linked color imaging in cases with endoscopic intestinal metaplasia (P = 0.0496) and lesions <20 mm in diameter (<10 mm, P = 0.002; 10-20 mm, P = 0.004). Lesion size and endoscopic intestinal metaplasia are associated with the visibility of early gastric cancer in white light imaging. Linked color imaging improves visibility of gastric cancer with these factors.

A New Encryption Algorithm Utilizing DNA Subsequence Operations for Color Images

The computer network has fundamentally transformed modern interactions, enabling the effortless transmission of multimedia data. However, the openness of these networks necessitates heightened attention to the security and confidentiality of multimedia content. Digital images, being a crucial component of multimedia communications, require robust protection measures, as their security has become a global concern. Traditional color image encryption/decryption algorithms, such as DES, IDEA, and AES, are unsuitable for image encryption due to the diverse storage formats of images, highlighting the urgent need for innovative encryption techniques. Chaos-based cryptosystems have emerged as a prominent research focus due to their properties of randomness, high sensitivity to initial conditions, and unpredictability. These algorithms typically operate in two phases: shuffling and replacement. During the shuffling phase, the positions of the pixels are altered using chaotic sequences or matrix transformations, which are simple to implement and enhance encryption. However, since only the pixel positions are modified and not the pixel values, the encrypted image’s histogram remains identical to the original, making it vulnerable to statistical attacks. In the replacement phase, chaotic sequences alter the pixel values. This research introduces a novel encryption technique for color images (RGB type) based on DNA subsequence operations to secure these images, which often contain critical information, from potential cyber-attacks. The suggested method includes two main components: a high-speed permutation process and adaptive diffusion. When implemented in the MATLAB software environment, the approach yielded promising results, such as NPCR values exceeding 98.9% and UACI values at around 32.9%, demonstrating its effectiveness in key cryptographic parameters. Security analyses, including histograms and Chi-square tests, were initially conducted, with passing Chi-square test outcomes for all channels; the correlation coefficient between adjacent pixels was also calculated. Additionally, entropy values were computed, achieving a minimum entropy of 7.0, indicating a high level of randomness. The method was tested on specific images, such as all-black and all-white images, and evaluated for resistance to noise and occlusion attacks. Finally, a comparison of the proposed algorithm’s NPCR and UAC values with those of existing methods demonstrated its superior performance and suitability.

A fast color image encryption scheme based on the new chaotic structure and dynamic strong S-boxes

A fast color image encryption scheme based on the new chaotic structure and dynamic strong S-boxes

Computation of Outer Inverse of Tensors Based on t‐Product

ABSTRACTTensor computations play an essential role in various fields of science and engineering, including multiway data analysis. In this study, we established a few basic properties of the range and null space of a tensor by using block circulant matrices and a discrete Fourier matrix. We then discuss the outer inverse of the tensors based on ‐product with a prescribed range and kernel of third‐order tensors. We address the relation of this outer inverse with other generalized inverses, such as the Moore–Penrose inverse, group inverse, and Drazin inverse. In addition, we present a few algorithms for computing the outer inverses of the tensors. In particular, a ‐QR decomposition based algorithm was developed to compute outer inverses. It is well known that the confidentiality of information transmitted through the virtual world grows exponentially, and color image and video security have become a significant concern when communicating over the internet. As an application, a ‐QR decomposition based algorithm was demonstrated for concealing secret color images and videos.

Relative Radiometric Correction Method Based on Temperature Normalization for Jilin1-KF02

The optical remote sensors carried by the Jilin-1 KF02 series satellites have an imaging resolution better than 0.5 m and a width of 150 km. There are radiometric problems, such as stripe noise, vignetting, and inter-slice chromatic aberration, in their raw images. In this paper, a relative radiometric correction method based on temperature normalization is proposed for the response characteristics of sensors and the structural characteristics of optical splicing of Jilin-1 KF02 series satellites cameras. Firstly, a model of temperature effect on sensor output is established to correct the variation of sensor response output digital number (DN) caused by temperature variation during imaging process, and the image is normalized to a uniform temperature reference. Then, the horizontal stripe noise of the image is eliminated by using the sensor scan line and dark pixel information, and the vertical stripe noise of the image is eliminated by using the method of on-orbit histogram statistics. Finally, the method of superposition compensation is used to correct the vignetting area at the edge of the image due to the lack of energy information received by the sensor so as to ensure the consistency of the image in color and image quality. The proposed method is verified by Jilin-1 KF02A on-orbit images. Experimental results show that the image response is uniform, the color is consistent, the average Streak Metrics (SM) is better than 0.1%, Root-Mean-Square Deviation of the Mean Line (RA) and Generalized Noise (GN) are better than 2%, Relative Average Spectral Error (RASE) and Relative Average Spectral Error (ERGAS) are greatly improved, which are better than 5% and 13, respectively, and the relative radiation quality is obviously improved after relative radiation correction.

Color image watermarking using vector SNCM-HMT

An image watermarking scheme is typically evaluated using three main conflicting characteristics: imperceptibility, robustness, and capacity. Developing a good image watermarking method is challenging because it requires a trade-off between these three basic characteristics. In this paper, we proposed a statistical color image watermarking based on robust discrete nonseparable Shearlet transform (DNST)-fast quaternion generic polar complex exponential transform (FQGPCET) magnitude and vector skew-normal-Cauchy mixtures (SNCM)-hidden Markov tree (HMT). The proposed watermarking system consists of two main parts: watermark inserting and watermark extraction. In watermark inserting, we first perform DNST on R, G, and B components of color host image, respectively. We then compute block FQGPCET of DNST domain color components, and embed watermark signal in DNST-FQGPCET magnitudes using multiplicative approach. In watermark extraction, we first analyze the robustness and statistical characteristics of local DNST-FQGPCET magnitudes of color image. We then observe that, vector SNCM-HMT model can capture accurately the marginal distribution and multiple strong dependencies of local DNST-FQGPCET magnitudes. Meanwhile, vector SNCM-HMT parameters can be computed effectively using variational expectation–maximization (VEM) parameter estimation. Motivated by our modeling results, we finally develop a new statistical color image watermark decoder based on vector SNCM-HMT and maximum likelihood (ML) decision rule. Experimental results on extensive test images demonstrate that the proposed statistical color image watermarking provides a performance better than that of most of the state-of-the-art statistical methods and some deep learning approaches recently proposed in the literature.

Revisiting Representation Learning of Color Information: Color Medical Image Segmentation Incorporating Quaternion

Currently, color medical image segmentation methods commonly extract color and texture features mixed together by default, however, the distribution of color information and texture information is different: color information is represented differently in different color channels of a color image, while the distribution of texture information remains the same. Such a simple and brute-force feature extraction pattern will inevitably result in a partial bias in the model's semantics understanding. In this paper, we decouple the representation learning for color-texture information, and propose a novel network for color medical image segmentation, named CTNet. Specifically, CTNet introduces the Quaternion CNN (QCNN) module to capture the correlation among different color channels of color medical images to generate color features, and uses a designed local-global texture feature integrator (LoG) to mine the textural features from local to global. Moreover, a multi-stage features interaction strategy is proposed to minimize the semantic understanding gap of color and texture features in CTNet, so that they can be subsequently fused to generate a unified and robust feature representation. Comparative experiments on four different color medical image segmentation benchmark datasets show that CTNet strikes an optimal trade-off between segmentation accuracy and computational overhead when compared to current state-of-the-art methods. We also conduct extensive ablation experiments to verify the effectiveness of the proposed components. Our code will be available at https://github.com/Notmezhan/CTNet.

Dequantenhancement by spatial color algorithms

Spatial color algorithms (SCAs) are algorithms grounded in the retinex theory of color sensation that, mimicking the human visual system, perform image enhancement based on the spatial arrangement of the scene. Despite their established role in image enhancement, their potential as dequantizers has never been investigated. Here, we aim to assess the effectiveness of SCAs in addressing the dual objectives of color dequantization and image enhancement at the same time. To this end, we propose the term dequantenhancement. In this paper, through two experiments on a dataset of images, SCAs are evaluated through two distinct pathways: first, quantization followed by filtering to assess both dequantization and enhancement; and second, filtering applied to original images before quantization as further investigation of mainly the dequantization effect. The results are presented both qualitatively, with visual examples, and quantitatively, through metrics including the number of colors, retinal-like subsampling contrast (RSC), and structural similarity index (SSIM).

Multiple Color Image Encryption System Based on Hybrid Digital/Optical Techniques and Assisted by 18D Memristive Chaos

Multiple Color Image Encryption System Based on Hybrid Digital/Optical Techniques and Assisted by 18D Memristive Chaos

A new controllable multi-wing chaotic system: applications in high-security color image encryption

A new controllable multi-wing chaotic system: applications in high-security color image encryption

Large-scale high purity and brightness structural color generation in layered thin film structures via coupled cavity resonance

Abstract Structural colors, resulting from the interaction of light with nanostructured materials rather than pigments, present a promising avenue for diverse applications ranging from ink-free printing to optical anti-counterfeiting. Achieving structural colors with high purity and brightness over large areas and at low costs is beneficial for many practical applications, but still remains a challenge for current designs. Here, we introduce a novel approach to realizing large-scale structural colors in layered thin film structures that are characterized by both high brightness and purity. Unlike conventional designs relying on single Fabry–Pérot cavity resonance, our method leverages coupled resonance between adjacent cavities to achieve sharp and intense transmission peaks with significantly suppressed sideband intensity. We demonstrate this approach by designing and experimentally validating transmission-type red, green, and blue colors using an Ag/SiO2/Ag/SiO2/Ag configuration on fused silica substrate. The measured spectra exhibit narrow resonant linewidths (full width at half maximum ∼60 nm), high peak efficiencies (&gt;40 %), and well-suppressed sideband intensities (∼0 %). In addition, the generated color can be easily tuned by adjusting the thickness of SiO2 layer, and the associated color gamut coverage shows a wider range than many existing standards. Moreover, the proposed design method is versatile and compatible with various choices of dielectric and metallic layers. For instance, we demonstrate the production of angle-robust structural colors by utilizing high-index Ta2O5 as the dielectric layer. Finally, we showcase a series of printed color images based on the proposed structures. The coupled-cavity-resonance architecture presented here successfully mitigates the trade-off between color brightness and purity in conventional layered thin film structures and provides a novel and cost-effective route towards the realization of large-scale and high-performance structural colors.

Study of the Dispersion Compensation Double-Layer Diffractive Optical Components Based on Metasurface and Grating, and Their Application in Augmented Reality Displays

We employed a double-layer coupled diffractive optical element, based on metasurfaces and diffraction gratings, which exhibits wavefront modulation and chromatic dispersion compensation. Utilizing this double-layer coupled diffractive optical element in the optical information transmission process of a diffractive waveguide allows for the transmission of color image information using a single-layer waveguide structure. Our results demonstrate that, under the conditions of a field of view of 47° × 47°, an entrance pupil size of 2.9 × 2.9 mm2, and an exit pupil extension size of 8.9 mm, the uniformity of the brightness for each monochromatic field reached 85%, while the uniformity of color transmission efficiency exceeded 95%.

Full van der Waals Ambipolar Ferroelectric Configurable Optical Hetero-Synapses for In-Sensor Computing.

The rapid development of visual neuromorphic hardware can be attributed to their ability to capture, store and process optical signals from the environment. The main limitation of existing visual neuromorphic hardware is that the realization of complex functions is premised on the increase of manufacturing cost, hardware volume and energy consumption. In this study, we demonstrated an optical synaptic device based on a three-terminal van der Waals (vdW) heterojunction that can realize the sensing functions of light wavelength and intensity as well as short-term and long-term synaptic plasticity. In the image recognition task, we constructed an optical reservoir neural network (ORNN) and a visible light communication system (VLC) composed of this optical synaptic device. The ORNN has a recognition rate of up to 84.9% for 50 000 color images in 10 categories in the CIFAR-10 color image dataset, and the VLC system can achieve high-speed transmission with an ultra-low power consumption of only 0.4 nW. This work shows that through reasonable design, vdW heterojunction structures have great application potential in low-power multifunctional fusion application tasks such as visual bionics.

Feature fusion and context interaction for RGB-D indoor semantic segmentation

Depth information in indoor scenes is crucial to understanding the spatial and occluding relationships of objects. Depth information contains rich geometric details and is unaffected by lighting, texture, and color, significantly enhancing traditional color images. RGB images and depth images have been widely applied in various image analysis. However, effectively leveraging the complementary information from both modalities remains a challenge. To address this issue, we propose a unified and effective feature fusion and context interaction network (FCINet). It includes three modules. Firstly, we construct a multi-modal feature fusion module (MFFM), which can achieve the aggregation of two modalities along spatial and channel dimensions. Secondly, we construct a global and local information interaction context module (GLCM) to encode rich semantic information. Finally, we construct a feature alignment and fusion module (FAFM), which integrates upsampled low-level features with high-level features to mitigate spatial information loss. Experimental results indicate that the model achieved favorable outcomes on both the NYU Depth v2 dataset and the more complex SUN RGB-D dataset.

DDNet: Depth Dominant Network for Semantic Segmentation of RGB-D Images

Convolutional neural networks (CNNs) have been widely applied to parse indoor scenes and segment objects represented by color images. Nonetheless, the lack of geometric and context information is a problem for most RGB-based methods, with which depth features are only used as an auxiliary module in RGB-D semantic segmentation. In this study, a novel depth dominant network (DDNet) is proposed to fully utilize the rich context information in the depth map. The critical insight is that obvious geometric information from the depth image is more conducive to segmentation than RGB data. Compared with other methods, DDNet is a depth-based network with two branches of CNNs to extract color and depth features. As the core of the encoder network, the depth branch is given a larger fusion weight to extract geometric information, while semantic information and complementary geometric information are provided by the color branch for the depth feature maps. The effectiveness of our proposed depth-based architecture has been demonstrated by comprehensive experimental evaluations and ablation studies on challenging RGB-D semantic segmentation benchmarks, including NYUv2 and a subset of ScanNetv2.